MS - Health Informatics Concentration

The science of health informatics drives innovation-defining future approaches to information and knowledge management in biomedical research, clinical care, and public health. Health informatics (HI) comprises applied research and the practice of informatics across clinical and public health domains. Informatics researchers develop, introduce, and evaluate new biomedically motivated methods in areas as diverse as data mining, natural language or text processing, cognitive science, human-computer interaction, decision support, databases and algorithms for analyzing large amounts of data generated in public health, clinical research and genomics/proteomics.

The MS degree will provide well-rounded training in Health Informatics, with a balance of core courses from such areas as information sciences, clinical informatics, clinical research informatics, consumer health and population health informatics, data science and more broadly health policy, social and behavioral science, biostatistics and epidemiology. The length of study for the MS in HI is two academic years. First-year courses survey the field; the typical second-year courses are more technical and put greater emphasis on mastering the skills in health informatics. The degree also requires a capstone project in the second year.

Applicants should typically have an undergraduate degree with a focus in health, computer science or mathematics/statistics. Students with a master’s degree or other related degrees may be allowed to enroll in additional elective courses in lieu of required courses, if they can demonstrate prior proficiency in required courses.

The length of study for the MS in Health Informatics is two years. Part-time enrollment is not an option.

This program does not require General GRE scores.

• BIS 633 Population and Public Health Informatics - 1 unit
  
• BIS 634 Computational Methods for Informatics - 1 unit
  
• CBB 740/BIS 560 Introduction to Health Informatics - 1unit
  
• CBB 750/BIS 550 Topics in Biomedical Informatics and Data Science- 1 unit
  
• EPH 508 (fall) Foundations of Epidemiology and Public Health or EPH 509 (spring) Fundamentals of Epidemiology - 1 unit
• EPH 608 Frontiers of Public Health* - 1 unit
  
• BIS 638 Clinical Database Management Systems and Ontologies - 1 unit
  
• BIS 562 Clinical Decision Support or BIS 640 User-Centered Design of Digital Health - 1 unit
  
• EPH 600 Research Ethics and Responsibilities - 0 units
• BIS 685/BIS 686 Capstone in Health Informatics- 2 units

• BIS 540 Fundamentals of Clinical Trials - 1 unit
• BIS 557 Computational Statistics - 1 unit
• BIS 567 Bayesian Statistics - 1 unit
• BIS 620 Data Science Software Systems - 1 unit
• BIS 621 Regression Models - 1 unit
• BIS 628 Longitudinal and Multilevel Data Analysis - 1 unit
• BIS 630 Applied Survival Analysis - 1 unit
• BIS 691 Theory of Generalized Linear Models - 1 unit
• BIS 568 Applied Machine Learning in Healthcare - 1 unit
• CB&B 555 Unsupervised Learning for Big Data - 1 unit
• CB&B 567 Topics in Deep Learning: Methods and Biomedical Applications - 1 unit
• CB&B 663 Deep Learning Theory and Applications - 1 unit
• CB&B 745 Advanced Topics in Machine Learning - 1 unit
• CB&B 645 Statistical Methods in Computational Biology - 1 unit
• CDE/EHS 566 Causal Inference Methods in Public Health Research - 1 unit
• CPSC 546 Data and Information Visualization - 1 unit
• CPSC 577 Natural Language Processing - 1 unit
• CPSC 564 Algorithms and their Societal Implications - 1 unit
• CPSC 582 Current Topics in Applied Machine Learning - 1 unit
• EMD 533 Implementation Science - 1 unit
• EPH 510 Health Policy and Health Care Systems - 1 unit
• HPM 560 Health Economics and U.S. Health Policy - 1 unit
• HPM 570 Cost-Effectiveness Analysis and Decision-Making - 1 unit
• IMED 625 Principles of Clinical Research - 1 unit
• MGT 534 Personal Leadership: Leading the Self Before Others - 1 unit
• MGT 656 Managing Software Development - 1 unit
• S&DS 517 Applied Machine Learning and Causal Inference - 1 unit
• S&DS 530 Data Exploration and Analysis - 1 unit
• S&DS 565 Introductory Machine Learning - 1 unit
• S&DS 562 Computational Tools for Data Science - 1 unit
• S&DS 583 Time Series with R/Python - 1 unit
• S&DS 584 Applied Graphical Models - 1 unit
• S&DS 610 Statistical Inference - 1 unit
• S&DS 663 Computational Methods for Data Science - 1 unit
• S&DS 664 Information Theory - 1 unit
• S&DS 670 Theory of Deep Learning - 1 unit

*Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

rev. 09.08.22