This is archival data. This system was retired as of August 21, 2023 and the information on this page has not been updated since then. For current information, visit catalogs.umn.edu.
Requirements for this program are current for
Spring 2018
Length of program in credits:
36
This program does not require summer semesters for timely completion.
Degree: Master of Science
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 is an interdisciplinary field of scholarship that applies computer, information, and cognitive sciences to promote the effective and efficient use and analysis of information, ultimately improving the health, well-being, and economic functioning of society. Students take a sequence of core courses in health informatics and biostatistics, and electives in technical and health science areas. Possible areas of emphasis include health information systems, telehealth, bioinformatics, user interface design, system impact evaluation, database construction and analysis, clinical decision-making, evaluation of health programs, and physiological monitoring and control.
The MS is a 36 credit degree that may be completed in as little as two years or up to five years. It is intended for students who are interested in research, but who do not have the background or are not ready to commit to the PhD program.
There are two kinds of MS degrees: MS Plan A and MS Plan B. The Plan A culminates in a substantial, 10-credit master's thesis. The Plan B culminates in a smaller, 4-credit, Plan B project. Electives comprise the additional six credits in the Plan B degree.
Program Delivery
via classroom (the majority of instruction is face-to-face)
partially online (between 50% to 80% of instruction is online)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program
is 3.30.
Applicants are expected to have at least a bachelor of science or equivalent degree from a regionally accredited institution of higher education.
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
Programming Language
Documented work or educational experience working with a 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
Department Consent
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.
or
HINF 8777 - Thesis Credits: Master's
(1.0-18.0 cr)
Electives
Graduate-level electives of your choice; see student handbook for a list of recommended electives. Plan A students will need 4 credits of electives, and Plan B students will need 10 credits of electives.
Program Sub-plans
A sub-plan is not required for this program.
Students may not complete the program with more than one sub-plan.
One Stop for tuition, course registration, financial aid, academic calendars, and more
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
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 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 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 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
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
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
HINF 8777 - Thesis Credits: Master's
Credits:
1.0
-18.0
[max 50.0]
Grading Basis:
No Grade
Typically offered:
Every Fall, Spring & Summer
(No description)
prereq: Max 18 cr per semester or summer; 10 cr total required [Plan A only]
BIOC 8002 - Molecular Biology and Regulation of Biological Processes
Credits:
3.0
[max 3.0]
Grading Basis:
A-F only
Typically offered:
Every Fall
Classical to current topics in molecular biology. Aspects of DNA, RNA, and protein biology. DNA replication, repair, and recombination. RNA transcription, editing, and regulation. Protein translation/modification. Technologies such as deep-sequencing micro-RNA and prions.
prereq: [BMBB or MCDBG] grad student or instr consent
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 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 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
This course is designed to introduce the high throughput platforms to students who are interested in the genomics research and genomics data analysis in the basic and clinical medical science field. The course covers history of the genomics platforms, its revolution and the specifics of the data generated by all existing different platforms. The course will also introduce all existing sequencing platforms and applications to biological science, as well the current trends in this field.
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.
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 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 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 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 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
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 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 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 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 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 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
