Twin Cities campus

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Twin Cities Campus

Health Informatics Ph.D.

Health Informatics, AHC Inst
Graduate School
Link to a list of faculty for this program.
Contact Information
Physical Address: 505 Essex St. SE, 330 Diehl Hall, Minneapolis, MN 55455 Mailing Address: MMC 912, 420 Delaware St. SE, Minneapolis, MN 55455
Email: ihi@umn.edu
Website: http://healthinformatics.umn.edu
  • Program Type: Doctorate
  • Requirements for this program are current for Fall 2017
  • Length of program in credits: 70
  • This program does not require summer semesters for timely completion.
  • Degree: Doctor of Philosophy
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
Health informatics (also known as biomedical informatics) is an interdisciplinary field of scholarship that applies computer, information, statistical, management, and related scientific methods to enable biomedical discovery and support the effective and efficient use and analysis of data, management of information, and application of knowledge across the spectrum from basic science to clinical care. The ultimate goal of the field is to improve the health, well-being, and economic functioning of society. Students take a sequence of core courses in health informatics, computing, and biostatistics, and electives in technical and health science areas, and pursue one of four tracks: Data Science and Informatics for Learning Health Systems; Clinical Informatics; Translational Bioinformatics; or Precision and Personalized Medicine (PPM) Informatics. Students pursuing the Data Science and Informatics for Learning Health Systems track are expected to complete the University’s Data Science MS degree en route to the PhD. Students pursuing any of the other three tracks are expected to complete the Health Informatics MS degree en route to the PhD. Phase I is the MS phase, and Phase II is the PhD phase of the program. Phase II is completed after students have earned the MS degree. Students who have an MS in Data Science or Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to Academic Program Committee (APC) review and approval.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.50.
Applicants must have a BS or equivalent in science, technology, engineering, computer science, math, or another pertinent field from a regionally accredited university or international equivalent.
Required prerequisites
Health or Biological Sciences
6-semester credits or 9 quarter-credits of health or biological coursework at the undergraduate or graduate level or department consent.
Computer Science
Clinical Informatics Track
Documented work or educational experience working with a general purpose programming language such as C, C++, Java, Visual Basic, PASCAL, etc.
or HINF 5502 - Python Programming Essentials for the Health Sciences (1.0 cr)
or Other Tracks
Applicants to the Data Science for Learning Health Systems, Translational Bioinformatics, and Precision and Personalized Medicine Informatics tracks must also have taken an introduction to data structures and algorithms, such as the course listed below.
CSCI 1933 - Introduction to Algorithms and Data Structures (4.0 cr)
Track-Specific Prerequisites
Applicants to the Data Science for Learning Health Systems, Translational Bioinformatics, and Precision and Personalized Medicine Informatics tracks must also have the following prerequisites or must take remedial courses at the discretion of the admissions committee:
Mathematics
Applicants must have college-level calculus and linear algebra, such as the courses listed below.
MATH 1271 - Calculus I [MATH] (4.0 cr)
CSCI 2033 - Elementary Computational Linear Algebra (4.0 cr)
or MATH 4242 - Applied Linear Algebra (4.0 cr)
Statistics
Applicants must have college-level statistics, such as the courses below.
STAT 3011 - Introduction to Statistical Analysis [MATH] (4.0 cr)
or STAT 3021 - Introduction to Probability and Statistics (3.0 cr)
Applicants must submit their test score(s) from the following:
  • GRE
    • General Test - Verbal Reasoning: 152
    • General Test - Quantitative Reasoning: 159
    • General Test - Analytical Writing: 4.0
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
    • Paper Based - Total Score: 550
  • IELTS
    • Total Score: 6.5
    • Reading Score: 6.5
    • Writing Score: 6.5
  • MELAB
    • Final score: 80
The preferred English language test is Test of English as Foreign Language.
Key to test abbreviations (GRE, TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
46 credits are required in the major.
24 thesis credits are required.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 2 semesters must be completed before filing a Degree Program Form.
All courses taken, milestones met, and progress made in the program are subject to Academic Program Committee (APC) review. The inclusion of 4xxx-level coursework requires APC approval.
Required Core Coursework (14 credits)
Phase I (12 credits)
All students take the following core coursework for a total of 12 credits. HINF 5436 must be taken twice.
HINF 5430 - Foundations of Health Informatics I (3.0 cr)
HINF 8430 - Foundations of Health Informatics I Lab (2.0 cr)
HINF 5436 - AHC Informatics Grand Rounds (1.0 cr)
HINF 5440 - Foundations of Translational Bioinformatics (3.0 cr)
HINF 8440 - Foundations of Translational Bioinformatics Lab (2.0 cr)
Phase II (2 credits)
All students take the following core course after completing the Phase I core, and with the approval of the APC.
HINF 8525 - Health Informatics Teaching (2.0 cr)
Doctoral Thesis Credits (24 credits)
All students must take at least 24 doctoral thesis credits, in consultation with the APC.
HINF 8888 - Thesis Credit: Doctoral (1.0-24.0 cr)
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Clinical Informatics
The Clinical Informatics track provides instruction and training for students interested in clinical applications methods and applications. The curriculum includes instruction in health data and coding, systems analysis, human-computer interaction, current informatics research, and current applications such as decision support systems, natural language processing, and predictive modeling. Additionally, students learn biostatistical methods, relational database theory and practice, analytics and data science methodologies, consumer health informatics, and interprofessional practice. Electives supplement individual student interests in areas such as computer programming, health data management, health care finance, and public and population health (with scope to include person-empowered participation and inter-professional engagement). Courses use a mixture of theoretical and applied subject matter to provide a solid grounding in current informatics thinking and practice.
Students who pursue the Clinical Informatics track must complete the Health Informatics MS degree en route to completing the PhD. Students must consult with the APC to coordinate completion of coursework and other requirements for the Health Informatics MS, the Health Informatics PhD, and the Clinical Informatics track. Students who have an MS in Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to APC review and approval.
Clinical Informatics Coursework (32 credits)
Core Coursework (15 credits)
Take the following core courses:
HINF 5431 - Foundations of Health Informatics II (3.0 cr)
HINF 8431 - Foundations of Health Informatics II Lab (2.0 cr)
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation (3.0 cr)
HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety (2.0 cr)
HINF 5531 - Health Data Analytics and Data Science (3.0 cr)
HINF 5496 - Internship in Health Informatics (1.0-6.0 cr)
NURS 5116 - Consumer Health Informatics (2.0 cr)
NURS 7108 - Population Health Informatics (2.0 cr)
Required Biostatistics Coursework (8 credits)
Take the following two courses:
PUBH 6450 - Biostatistics I (4.0 cr)
PUBH 6451 - Biostatistics II (4.0 cr)
Elective Coursework (9 credits)
Select at least 9 elective credits, in consultation with the APC, to complete the 46 course credits required for the PhD degree.
Data Science and Informatics for Learning Health Systems
The Data Science and Informatics for Learning Health Systems track builds on the highly regarded data science program offered jointly by the School of Engineering, School of Public Health, and School of Statistics. It also takes advantage of School of Nursing's breadth of nursing and health informatics courses. It requires students to fulfill the requirements of the Masters in Data Science program and use their elective courses to gain exposure to health sciences and health care in the form of a suite of required foundational courses: Foundations of Health Informatics I and Lab, Foundations of Translational Bioinformatics I and Lab and the US Health Care System offered by the Institute for Health Informatics. The MS capstone project will address a research question related to health sciences or healthcare. Specialization to the health care field intensifies at the PhD level by offering additional courses focusing on advanced analytics and its applications to healthcare. The thesis research will naturally relate to health science or healthcare.
Students who pursue the Data Science and Informatics for Learning Health Systems track are expected to earn the University’s Data Science MS degree en route to completing the PhD. Students must consult with the APC to coordinate completion of coursework and other requirements for the Data Science MS, the Health Informatics PhD, and the Data Science and Informatics for Learning Health Systems track. Credits earned in the University’s Data Science MS program may be used to fulfill required courses or elective credits in the Data Science and Informatics for Learning Health Systems track, subject to APC approval. Students who have an MS in Data Science from a comparable program may be exempt from this requirement in whole or in part, subject to APC review and approval.
Data Science and Informatics Coursework (32 credits)
Core Coursework (18 credits)
Take the following courses, in consultation with the APC, after completion of the Data Science MS degree.
HINF 5496 - Internship in Health Informatics (1.0-6.0 cr)
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation (3.0 cr)
HINF 5630 - Clinical Data Mining (3.0 cr)
HINF 8220 - Computational Causal Analytics (3.0 cr)
HINF 8492 - Advanced Readings or Research in Health Informatics (1.0-6.0 cr)
Elective Coursework (14 credits)
Select at least 14 elective credits from the following list, in consultation with the APC, to complete the 46 course credits required for the PhD degree. Credits earned in pursuit of the Data Science MS may be used to fulfill elective course requirements for this track, subject to APC approval.
Informatics
HINF 5431 - Foundations of Health Informatics II (3.0 cr)
HINF 8431 - Foundations of Health Informatics II Lab (2.0 cr)
HINF 5610 - Foundations of Biomedical Natural Language Processing (3.0 cr)
HINF 5620 - Data Visualization for the Health Sciences (3.0 cr)
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
Applications
NURS 7113 - Clinical Decision Support: Theory (2.0 cr)
PUBH 6102 - Issues in Environmental Health (2.0 cr)
PUBH 6560 - Operations Research and Quality in Health Care (3.0 cr)
PUBH 6717 - Decision Analysis for Health Care (2.0 cr)
PUBH 6751 - Principles of Management in Health Services Organizations (2.0 cr)
PUBH 6765 - Continuous Quality Improvement: Methods and Techniques (3.0 cr)
PUBH 6809 - Advanced Methods in Health Decision Science (3.0 cr)
PUBH 6814 {Inactive} (2.0 cr)
PUBH 6862 - Cost-Effectiveness Analysis in Health Care (3.0 cr)
PUBH 6876 {Inactive} (2.0 cr)
Advanced Methodology
PUBH 6341 - Epidemiologic Methods I (3.0 cr)
PUBH 8452 - Advanced Longitudinal Data Analysis (3.0 cr)
PUBH 8462 - Advanced Survival Analysis (3.0 cr)
PUBH 8472 - Spatial Biostatistics (3.0 cr)
Translational Bioinformatics
The Translational Bioinformatics track bridges genomics and bioinformatics to precision medicine through its methods and techniques development and innovation that directly relate to the study of basic biological science and diseases. The computational methods related to genomics, epigenomics, transcriptomics, proteomics, metabolomics and pharmacogenomcis are included, which build the connection of molecular findings and phenotypes to characterize disease susceptibility or determine disease markers, and predict response to treatment and prognosis. The program offers three specialized areas: structural and functional genomics, microbiomics and metagenomics, and cancer genomics.
Students pursuing the Translational Bioinformatics track are expected to earn the Health Informatics MS degree en route to completing the PhD. Students must consult with the APC to coordinate completion of coursework and other requirements for the Health Informatics MS, the Health Informatics PhD, and the Translational Bioinformatics track. Students who have an MS in Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to APC review and approval.
Translational Bioinformatics Coursework (32 credits)
Phase 1 (22 credits)
Take the following courses for a total of 22 credits:
CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
HINF 8220 - Computational Causal Analytics (3.0 cr)
HINF 5650 - Integrative Genomics and Computational Methods (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling (3.0 cr)
BIOC 8007 - Molecular Biology of the Genome (2.0 cr)
BIOC 8008 - Molecular Biology of the Transcriptome (2.0 cr)
Phase II (6 credits)
Take the following courses after competing Phase I, and with the approval of the APC:
HINF 5496 - Internship in Health Informatics (1.0-6.0 cr)
HINF 8492 - Advanced Readings or Research in Health Informatics (1.0-6.0 cr)
Elective Coursework (4 credits)
Select at least 4 elective credits from the following list, in consultation with the APC, to complete the 46 course credits required for the PhD degree.
HINF 5431 - Foundations of Health Informatics II (3.0 cr)
HINF 8431 - Foundations of Health Informatics II Lab (2.0 cr)
HINF 5450 - Foundations of Precision Medicine Informatics (3.0 cr)
HINF 5610 - Foundations of Biomedical Natural Language Processing (3.0 cr)
MEDC 5245 - Introduction to Drug Design (3.0 cr)
PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine (2.0 cr)
PUBH 7415 - Introduction to Clinical Trials (3.0 cr)
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 8445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression (3.0 cr)
Precision and Personalized Medicine Informatics
The Precision and Personalized Medicine Informatics track provides a didactic program for students training in informatics who will develop specialized knowledge in precision informatics methods applied to personal and population health-focused problems. The scope of this track includes social determinants of health and inter-professional research and expertise. Students will develop skills in quantitative methods and biomedical sciences for their application to precision medicine. In addition, students will gain an understanding of medical and biological science to provide needed context on which to apply informatics methods.
Students who pursue the Precision and Personalized Medicine Informatics track are expected to earn the Health Informatics MS degree en route to completing the PhD. Students must consult with the APC to coordinate completion of coursework and other requirements for the Health Informatics MS, the Health Informatics PhD, and the Precision and Personalized Medicine Informatics track. Students who have an MS in Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to APC review and approval.
Precision and Personalized Medicine Informatics Coursework (32 credits)
Phase I (18 - 19 credits)
Take the following coursework for at least 18 credits.
HINF 5450 - Foundations of Precision Medicine Informatics (3.0 cr)
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation (3.0 cr)
HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety (2.0 cr)
HINF 8770 - Plan B Project (4.0 cr)
PUBH 7401 - Fundamentals of Biostatistical Inference (4.0 cr)
PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)
HINF 5531 - Health Data Analytics and Data Science (3.0 cr)
or HINF 5630 - Clinical Data Mining (3.0 cr)
Phase II (8 credits)
Take the following courses after completing Phase I, and with the approval of the APC.
HINF 5496 - Internship in Health Informatics (1.0-6.0 cr)
HINF 8492 - Advanced Readings or Research in Health Informatics (1.0-6.0 cr)
PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine (2.0 cr)
Elective Coursework (5 -6 credits)
Select at least 5 elective credits, in consultation with the APC, to complete the 46 course credits required for the PhD degree.
HINF 5431 - Foundations of Health Informatics II (3.0 cr)
MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
MATH 5445 - Mathematical Analysis of Biological Networks (4.0 cr)
PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 8432 - Probability Models for Biostatistics (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
PUBH 8445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 8446 - Advanced Statistical Genetics and Genomics (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
 
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HINF 5502 - Python Programming Essentials for the Health Sciences
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent
CSCI 1933 - Introduction to Algorithms and Data Structures
Credits: 4.0 [max 4.0]
Course Equivalencies: CSci 1902/CSci 1933/CSci 1933H
Typically offered: Every Fall, Spring & Summer
Advanced object oriented programming to implement abstract data types (stacks, queues, linked lists, hash tables, binary trees) using Java language. Inheritance. Searching/sorting algorithms. Basic algorithmic analysis. Use of software development tools. Weekly lab. prereq: 1133 or instr consent
MATH 1271 - Calculus I (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 1271/Math 1381/Math 1571/
Typically offered: Every Fall, Spring & Summer
Differential calculus of functions of a single variable, including polynomial, rational, exponential, and trig functions. Applications, including optimization and related rates problems. Single variable integral calculus, using anti-derivatives and simple substitution. Applications may include area, volume, work problems. prereq: 4 yrs high school math including trig or satisfactory score on placement test or grade of at least C- in [1151 or 1155]
CSCI 2033 - Elementary Computational Linear Algebra
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Matrices/linear transformations, basic theory. Linear vector spaces. Inner product spaces. Systems of linear equations, Eigenvalues, singular values. Algorithms/computational matrix methods using MATLAB. Use of matrix methods to solve variety of computer science problems. prereq: [MATH 1271 or MATH 1371], [1113 or 1133 or knowledge of programming concepts]
MATH 4242 - Applied Linear Algebra
Credits: 4.0 [max 4.0]
Course Equivalencies: Math 4242/Math 4457
Typically offered: Every Fall, Spring & Summer
Systems of linear equations, vector spaces, subspaces, bases, linear transformations, matrices, determinants, eigenvalues, canonical forms, quadratic forms, applications. prereq: 2243 or 2373 or 2573
STAT 3011 - Introduction to Statistical Analysis (MATH)
Credits: 4.0 [max 4.0]
Course Equivalencies: AnSc 3011/ESPM 3012/Stat 3011/
Typically offered: Every Fall, Spring & Summer
Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.
STAT 3021 - Introduction to Probability and Statistics
Credits: 3.0 [max 3.0]
Course Equivalencies: STAT 3021/STAT 3021H
Typically offered: Every Fall, Spring & Summer
This is an introductory course in statistics whose primary objectives are to teach students the theory of elementary probability theory and an introduction to the elements of statistical inference, including testing, estimation, and confidence statements. prereq: Math 1272
HINF 5430 - Foundations of Health Informatics I
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
An introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modeling; human-computer interaction and data visualization. Lectures, readings, and exercises highlight the intersections of these topics with electronic health record systems and other health information technology. prereq: Junior, senior, grad student, professional student, or instr consent
HINF 8430 - Foundations of Health Informatics I Lab
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
The PhD-level lab complement for introductory survey of health informatics, focusing on foundational concepts. Topics covered include: conceptualizations of data, information, and knowledge; current terminologies, coding, and classification systems for medical information; ethics, privacy, and security; systems analysis, process and data modeling; human-computer interaction and data visualization. Lectures, readings, and exercises highlight the intersections of these topics with electronic health record systems and other health information technology.
HINF 5436 - AHC Informatics Grand Rounds
Credits: 1.0 [max 10.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Presentation/discussion of research problems, current literature/topics of interest in Health Informatics.
HINF 5440 - Foundations of Translational Bioinformatics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics
HINF 8440 - Foundations of Translational Bioinformatics Lab
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics. Prerequisites: MS, PhD, or MD/PhD student interested in translational bioinformatics
HINF 8525 - Health Informatics Teaching
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Spring Even Year
Use selected teaching techniques to assist in the delivery of course content in health informatics curriculum. Work with a professor who is the course director. From evaluation and feedback on their teaching technique, students develop a teaching philosophy as a final course project. prereq: HINF student or instr consent
HINF 8888 - Thesis Credit: Doctoral
Credits: 1.0 -24.0 [max 100.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
(No description) prereq: PhD candidate or department consent. Max 18 credits per semester; 24 credits required
HINF 5431 - Foundations of Health Informatics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent
HINF 8431 - Foundations of Health Informatics II Lab
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
The PhD-level lab complement for an introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research.
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Principles of database theory, modeling, design, and manipulation of databases will be introduced, taught with a healthcare applications emphasis. Students will gain experience using a relational database management system (RDBMS), and database manipulation will be explored using Structured Query Language (SQL) to compose and execute queries. Students will be able to critically evaluate database query methods and results, and understand their implications for health care. prereq: Junior or senior or grad student or professional student or instr consent
HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
Application/operation of clinical information systems, electronic health records, decision support/application in health care system. Use of clinical information systems/association with health care delivery, payment, quality, outcomes. prereq: Junior or senior or grad student or professional student or instr consent
HINF 5531 - Health Data Analytics and Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Data science methods and techniques for the extraction, preparation, and use of health data in decision making. prereq: Junior or senior or professional student or grad student or instr consent
HINF 5496 - Internship in Health Informatics
Credits: 1.0 -6.0 [max 18.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall, Spring & Summer
Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent
NURS 5116 - Consumer Health Informatics
Credits: 2.0 [max 2.0]
Grading Basis: OPT No Aud
Typically offered: Every Fall
This course examines issues from the consumer?s perspective in the acquisition, understanding, or use of health information. Mobile health, telehealth, sensor technology, and internet sources for improving health are examined. The impact on consumer-provider communication and relationships as well as ethical and legal issues are explored. prereq: Grad student or instr consent
NURS 7108 - Population Health Informatics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall
Standards, interoperability, and integration of information systems for population health are examined. Population health use cases are analyzed for potential benefits, legal, ethical, and practical issues related to the development of population health information systems. prereq: [5115 or [HINF 5430, HINF 5431]] or instr consent
PUBH 6450 - Biostatistics I
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This course will cover the fundamental concepts of exploratory data analysis and statistical inference for univariate and bivariate data, including: ? study design and sampling methods, ? descriptive and graphical summaries, ? random variables and their distributions, ? interval estimation, ? hypothesis testing, ? relevant nonparametric methods, ? simple regression/correlation, and ? introduction to multiple regression. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [College-level algebra, health sciences grad student] or instr consent
PUBH 6451 - Biostatistics II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
This course will cover more advanced aspects of statistical analysis methods with a focus on statistical modeling, including: ? two-way ANOVA, ? multiple linear regression, ? logistic regression, ? Poisson regression, ? log binomial and ordinal regression, ? survival analysis methods, including Kaplan-Meier analysis and proportional hazards (Cox) regression, ? power and sample size, and ? survey sampling and analysis. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [PubH 6450 with grade of at least B, health sciences grad student] or instr consent
HINF 5496 - Internship in Health Informatics
Credits: 1.0 -6.0 [max 18.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall, Spring & Summer
Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Principles of database theory, modeling, design, and manipulation of databases will be introduced, taught with a healthcare applications emphasis. Students will gain experience using a relational database management system (RDBMS), and database manipulation will be explored using Structured Query Language (SQL) to compose and execute queries. Students will be able to critically evaluate database query methods and results, and understand their implications for health care. prereq: Junior or senior or grad student or professional student or instr consent
HINF 5630 - Clinical Data Mining
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Periodic Fall
This is a hands-on introductory data mining course specifically focusing on health care applications. Analogously to the relationship between biostatistics and statistics, the data and computational challenges, the experiment design and the model performance requirements towards data mining in the clinical domain differ from those in general applications. This course aims to teach the students the most common data mining techniques and elaborate on the differences between general and clinical data mining. Specifically, the course will focus on (i) clinical data challenges and preprocessing; (ii) survey of the most common techniques in the clinical domain; (iii) clinical application touching up on experimental design and collaborations with physicians. The class will meet twice a week, one day dedicated to lectures and one day to a hands-on lab component, where students are expected to apply the techniques to health-related data. Some of the models will be evaluated with the involvement of a physician collaborator. Prerequisites: Basic linear algebra (matrix notation), basic optimization (gradient descent) Graduate level introductory statistics (e.g. STAT 5101-5102) or equivalent or instructor consent
HINF 8220 - Computational Causal Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.
HINF 8492 - Advanced Readings or Research in Health Informatics
Credits: 1.0 -6.0 [max 24.0]
Grading Basis: OPT No Aud
Typically offered: Every Fall, Spring & Summer
Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent
HINF 5431 - Foundations of Health Informatics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent
HINF 8431 - Foundations of Health Informatics II Lab
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
The PhD-level lab complement for an introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research.
HINF 5610 - Foundations of Biomedical Natural Language Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
The course will provide a systematic introduction to basic knowledge and methods used in natural language processing (NLP) research. It will introduce biomedical NLP tasks and methods as well as their resources and applications in the biomedical domain. The course will also provide hands-on experience with existing NLP tools and systems. Students will gain basic knowledge and skills in handling with main biomedical NLP tasks. Prerequisites graduate student or instructor consent; Experience with at least one programming language (Python or Perl preferred) Recommended: basic understanding of data mining concepts, basic knowledge of computational linguistics
HINF 5620 - Data Visualization for the Health Sciences
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
An advanced health informatics course, focusing on theoretical and practical aspects of data and information visualization for health care and the health sciences. Topics include classic and novel visualization types; models of human visual perception and cognition; color, text and typography; maps and diagrams; evaluation and testing; and the aesthetic and cultural aspects of visualization. Examples emphasize health sciences applications for clinicians, patients, researchers, and analysts. Modern programming and commercial tools are discussed, including D3, ggplot2, and Tableau. Students will report on and discuss visualization methods, published studies and books, culminating in a final visualization project of the student's choosing.
MATH 5467 - Introduction to the Mathematics of Image and Data Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Background theory/experience in wavelets. Inner product spaces, operator theory, Fourier transforms applied to Gabor transforms, multi-scale analysis, discrete wavelets, self-similarity. Computing techniques. prereq: [2243 or 2373 or 2573], [2283 or 2574 or 3283 or instr consent]; [[2263 or 2374], 4567] recommended
NURS 7113 - Clinical Decision Support: Theory
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Principles and concepts of knowledge management and decision making for support of clinical practice. Students design a clinical decision support intervention and examine the legal, ethical, and practical issues related to its implementation and maintenance of CDS interventions. prereq: 5115 or HINF 5430/5431 or instr consent
PUBH 6102 - Issues in Environmental Health
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Current issues, principles, and methods of environmental/occupational health practice. prereq: Public health [MPH or MHA or certificate] student or health journalism MA major or nursing MS student or instr consent
PUBH 6560 - Operations Research and Quality in Health Care
Credits: 3.0 [max 3.0]
Prerequisites: Grad-level statistics/management coursework
Grading Basis: A-F only
Typically offered: Every Fall
Using a systems perspective to develop models to analyze/improve health care operations. Identifying data needs/sources to model structures, processes, and outcomes of care. Applying quality improvement, management sciences/operations research techniques to real world health care problems. prereq: Grad-level statistics/management coursework
PUBH 6717 - Decision Analysis for Health Care
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Introduction to methods/range of applications of decision analysis and cost-effectiveness analysis in health care technology assessment, medical decision making, and health resource allocation.
PUBH 6751 - Principles of Management in Health Services Organizations
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Understanding of and improvement in the competencies of managers in organizations, particularly as applied to health services and public health organizations. prereq: [Public hlth MPH or MHA or certificate] student or [environmental health MS or PhD] student or dentistry MS student or instr consent
PUBH 6765 - Continuous Quality Improvement: Methods and Techniques
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theory/practical applications of concepts, tools, techniques of continuous quality improvement (QI) in public health/health care.
PUBH 6809 - Advanced Methods in Health Decision Science
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Methods applicable to issues of medical decision making. Analyses of environmental/safety decisions. How to apply methods at cutting-edge of clinical decision science. prereq: [6717 or intro course in decision analysis], some facility with mathematical notation/reasoning
PUBH 6862 - Cost-Effectiveness Analysis in Health Care
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Government regulations. New technologies. Diagnosis/treatment protocols. Strengths, limitations, appropriateness of different approaches. prereq: instr consent; introductory econ course recommended
PUBH 6341 - Epidemiologic Methods I
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 6320PubH /6341
Grading Basis: A-F only
Typically offered: Every Fall
Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.
PUBH 8452 - Advanced Longitudinal Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Methods of inference for outcome variables measured repeatedly in time or space. Linear/nonlinear models with either normal or non-normal error structures. Random effects. Transitional/marginal models with biomedical applications. prereq: [Stat 5102, Stat 8311, experience with [SAS or S+], advanced [biostats or stat] student] or instr consent
PUBH 8462 - Advanced Survival Analysis
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Statistical methods for counting processes. Martingale theory (transforms, predictable processes, Doob decomposition, convergence, submartingales). Applications to nonparametric intensity estimation. Additive/relative risk models. Inference for event history data, recurrent events, multivariate survival, diagnostics. prereq: [7450, 8432, Stat 5102, advanced [biostatistics or statistics] major] or instr consent
PUBH 8472 - Spatial Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended
CSCI 5421 - Advanced Algorithms and Data Structures
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
HINF 8220 - Computational Causal Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.
HINF 5650 - Integrative Genomics and Computational Methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Spring
Genome-scale high throughput data sets are a central feature of modern biological research and translational clinical study. Experimental, computational biologists and clinical researchers who want to get the most from their data sets need to have a firm grasp and understanding of genomic data structure characteristics, analytical methodology and the intrinsic connection to integrate. This course is designed to build competence in quantitative methods for the analysis of high-throughput genomic data and data integration.
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent
STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Design experiments/analyze data with fixed effects, random/mixed effects models. ANOVA for factorial designs. Contrasts, multiple comparisons, power/sample size, confounding, fractional factorials. Computer-generated designs. Response surfaces. Multi-level models. Generalized estimating equations (GEE) for longitudinal data with non-normal errors. prereq: 8051 or instr consent
BIOC 8007 - Molecular Biology of the Genome
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.
BIOC 8008 - Molecular Biology of the Transcriptome
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This course explores the molecular biology of the eukaryotic genome and transcriptome, focusing on fundamental genetic processes, molecular mechanisms, and their relationships to biology and disease. Students gain a firm understanding of the key concepts and techniques through lectures, reading, and discussions. Students learn to critically analyze scientific papers through student-led presentations and discussions. They gain experience in articulating scientific questions, formulating testable hypotheses, and designing experiments. This course promotes development of science writing skills.
HINF 5496 - Internship in Health Informatics
Credits: 1.0 -6.0 [max 18.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall, Spring & Summer
Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent
HINF 8492 - Advanced Readings or Research in Health Informatics
Credits: 1.0 -6.0 [max 24.0]
Grading Basis: OPT No Aud
Typically offered: Every Fall, Spring & Summer
Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent
HINF 5431 - Foundations of Health Informatics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent
HINF 8431 - Foundations of Health Informatics II Lab
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
The PhD-level lab complement for an introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research.
HINF 5450 - Foundations of Precision Medicine Informatics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
The course will provide an introduction into the fundamental concepts of Precision Medicine with a focus on informatics-focused applications for clinical data representation, acquisition, decision making and outcomes evaluation. The student will gain an appreciation of fundamental biomedical data representation and its application to genomic, clinical, and population problems.
HINF 5610 - Foundations of Biomedical Natural Language Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
The course will provide a systematic introduction to basic knowledge and methods used in natural language processing (NLP) research. It will introduce biomedical NLP tasks and methods as well as their resources and applications in the biomedical domain. The course will also provide hands-on experience with existing NLP tools and systems. Students will gain basic knowledge and skills in handling with main biomedical NLP tasks. Prerequisites graduate student or instructor consent; Experience with at least one programming language (Python or Perl preferred) Recommended: basic understanding of data mining concepts, basic knowledge of computational linguistics
MEDC 5245 - Introduction to Drug Design
Credits: 3.0 [max 3.0]
Course Equivalencies: Chem 5245/Phar 6245/MedC 5245
Grading Basis: A-F or Aud
Typically offered: Every Fall
Concepts that govern design/discovery of drugs. Physical, bioorganic, medicinal chemical principles applied to explain rational design, mechanism of action drugs. prereq: Chem
PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course is for individuals wanting advanced knowledge in PGx and an introduction to the broader field of precision medicine. It consists of lectures, homework assignments and class discussions designed to introduce precision medicine as it relates to the impact of genetic variation on health and the advanced practice of pharmacogenomics. Students will learn diagnostic methods to identify germline and somatic mutations, how algorithms and equations are used for predictions and the relationships between genes and environment. How these concepts apply in the clinical settings, forecast health and drug response will be presented. Emerging tools in genetics and pharmacogenomics will be presented. The course will also address clinical implementation and the ethical, legal, and social issues presented by precision medicine and PGx. Prerequisite: 3rd year PharmD student, graduate student in healthcare or related program, or equivalent experience or instructor consent
PUBH 7415 - Introduction to Clinical Trials
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 3415/PubH 7415
Typically offered: Every Fall & Summer
Hypotheses/endpoints, choice of intervention/control, ethical considerations, blinding/randomization, data collection/monitoring, sample size, analysis, writing. Protocol development, group discussions. prereq: 6414 or 6450 or one semester graduate-level introductory biostatistics or statistics or instr consent
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to and methodology of randomized clinical trials. Design issues, sample size, operational details, interim monitoring, data analysis issues, overviews. prereq: 6451 or concurrent registration is required (or allowed) in 6451 or 7406 or instr consent
PUBH 8445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 7445/PubH 8445
Typically offered: Fall Odd Year
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable
STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression
Credits: 3.0 [max 3.0]
Prerequisites: PhD student in stat or DGS permission and 8052
Grading Basis: A-F or Aud
Typically offered: Every Fall
Standard multivariate analysis. Multivariate linear model, classification, clustering, principal components, factor analysis, canonical correlation. Topics in advanced regression. prereq: PhD student in stat or DGS permission and 8052
HINF 5450 - Foundations of Precision Medicine Informatics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
The course will provide an introduction into the fundamental concepts of Precision Medicine with a focus on informatics-focused applications for clinical data representation, acquisition, decision making and outcomes evaluation. The student will gain an appreciation of fundamental biomedical data representation and its application to genomic, clinical, and population problems.
HINF 5510 - Applied Health Care Databases: Database Principles and Data Evaluation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Principles of database theory, modeling, design, and manipulation of databases will be introduced, taught with a healthcare applications emphasis. Students will gain experience using a relational database management system (RDBMS), and database manipulation will be explored using Structured Query Language (SQL) to compose and execute queries. Students will be able to critically evaluate database query methods and results, and understand their implications for health care. prereq: Junior or senior or grad student or professional student or instr consent
HINF 5520 - Informatics Methods for Health Care Quality, Outcomes, and Patient Safety
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
Application/operation of clinical information systems, electronic health records, decision support/application in health care system. Use of clinical information systems/association with health care delivery, payment, quality, outcomes. prereq: Junior or senior or grad student or professional student or instr consent
HINF 8770 - Plan B Project
Credits: 4.0 [max 4.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Research project. Topic arranged between student/instructor. Written report required. prereq: Advanced plan B MS student
PUBH 7401 - Fundamentals of Biostatistical Inference
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Part of two-course sequence intended for PhD students in School of Public Health who need rigorous approach to probability/statistics/statistical inference with applications to research in public health. prereq: Background in calculus; intended for PhD students in public hlth and other hlth sci who need rigorous approach to probability/statistics and statistical inference with applications to research in public hlth
PUBH 7402 - Biostatistics Modeling and Methods
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Second of two-course sequence. Rigorous approach to probability/statistics, statistical inference. Applications to research in public health. prereq: 7401; intended for PhD students in health sciences
HINF 5531 - Health Data Analytics and Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Data science methods and techniques for the extraction, preparation, and use of health data in decision making. prereq: Junior or senior or professional student or grad student or instr consent
HINF 5630 - Clinical Data Mining
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Periodic Fall
This is a hands-on introductory data mining course specifically focusing on health care applications. Analogously to the relationship between biostatistics and statistics, the data and computational challenges, the experiment design and the model performance requirements towards data mining in the clinical domain differ from those in general applications. This course aims to teach the students the most common data mining techniques and elaborate on the differences between general and clinical data mining. Specifically, the course will focus on (i) clinical data challenges and preprocessing; (ii) survey of the most common techniques in the clinical domain; (iii) clinical application touching up on experimental design and collaborations with physicians. The class will meet twice a week, one day dedicated to lectures and one day to a hands-on lab component, where students are expected to apply the techniques to health-related data. Some of the models will be evaluated with the involvement of a physician collaborator. Prerequisites: Basic linear algebra (matrix notation), basic optimization (gradient descent) Graduate level introductory statistics (e.g. STAT 5101-5102) or equivalent or instructor consent
HINF 5496 - Internship in Health Informatics
Credits: 1.0 -6.0 [max 18.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall, Spring & Summer
Practical industrial experience not directly related to student's normal academic experience. prereq: HINF student or instr consent
HINF 8492 - Advanced Readings or Research in Health Informatics
Credits: 1.0 -6.0 [max 24.0]
Grading Basis: OPT No Aud
Typically offered: Every Fall, Spring & Summer
Directed readings or research in topics of current or theoretical interest in health informatics. prereq: HINF student or instr consent
PHAR 6224 - Advanced Pharmacogenomics and Precision Medicine
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course is for individuals wanting advanced knowledge in PGx and an introduction to the broader field of precision medicine. It consists of lectures, homework assignments and class discussions designed to introduce precision medicine as it relates to the impact of genetic variation on health and the advanced practice of pharmacogenomics. Students will learn diagnostic methods to identify germline and somatic mutations, how algorithms and equations are used for predictions and the relationships between genes and environment. How these concepts apply in the clinical settings, forecast health and drug response will be presented. Emerging tools in genetics and pharmacogenomics will be presented. The course will also address clinical implementation and the ethical, legal, and social issues presented by precision medicine and PGx. Prerequisite: 3rd year PharmD student, graduate student in healthcare or related program, or equivalent experience or instructor consent
HINF 5431 - Foundations of Health Informatics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
An introductory survey of health informatics, focusing on applications of informatics concepts and technologies. Topics covered include: health informatics research, literature, and evaluation; precision medicine; decision models; computerized decision support systems; data mining, natural language processing, social media, rule-based system, and other emerging technologies for supporting 'Big Data' applications; security for health care information handling. Lectures, readings, and exercises highlight the intersections of these topics with current information technology for clinical care and research. prereq: Junior, senior, grad student, professional student, or instr consent
MATH 5652 - Introduction to Stochastic Processes
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101
MATH 5445 - Mathematical Analysis of Biological Networks
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Development/analysis of models for complex biological networks. Examples taken from signal transduction networks, metabolic networks, gene control networks, and ecological networks. prereq: Linear algebra, differential equations
PUBH 7430 - Statistical Methods for Correlated Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS. prereq: Regression at the level of PubH 6451 or PubH 7405 or Stat 5302. Familiarity with basic matrix notation and operations (multiplication, inverse, transpose). Working knowledge of SAS or R (PubH 6420).
PUBH 7440 - Introduction to Bayesian Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent
PUBH 7445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended
PUBH 8432 - Probability Models for Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Three basic models used for stochastic processes in the biomedical sciences: point processes (emphasizes Poisson processes), Markov processes (emphasizes Markov chains), and Brownian motion. Probability structure and statistical inference studied for each process. prereq: [7450, 7407, Stat 5102, [advanced biostatstics or statistics] major] or instr consent
PUBH 8442 - Bayesian Decision Theory and Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent
PUBH 8445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 7445/PubH 8445
Typically offered: Fall Odd Year
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [[[Stat 8101, Stat 8102] or equiv], PhD student] or instr consent; some background with molecular biology desirable
PUBH 8446 - Advanced Statistical Genetics and Genomics
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Genetic mapping of complex traits in humans, modern population genetics with an emphasis on inference based observed molecular genetics data, association studies; statistical methods for low/high level analysis of genomic/proteomic data. Multiple comparison and gene network modeling. prereq: [7445, statistical theory at level of STAT 5101-2; college-level molecular genetics course is recommended] or instr consent
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.