Course Descriptions

Approved Graduate Precision Health Certificate Courses

In order to provide a unique breadth and depth across disciplines, Precision Health courses are offered in three complementary components to provide a comprehensive understanding of Discovery (D), Treatment (T) and Health (H). Each course below is categorized in one or more of the three domains indicated by D, T, and/or H listed after the course description.


Precision Health Seminar 

LHS 9XX: Precision Health Seminar - As part of this weekly seminar, students may present their research and interact with precision health speakers in an interdisciplinary fashion. Additionally, students will reflect on the state of precision health through various reflection exercises and interactive journal clubs. 0.5 credit. D, T, H

Ethical, Legal, and Social Implications of Precision Health

HBHE 669: Genetics, Health Behavior, & Health Education - This course addresses the following topics: genetics and risk communication; ethical issues in genetics research; the psychological and behavioral impact of genetic testing; public and professional knowledge and attitudes about genetics; health education needs in genetics; and emerging issues in the field (e.g., computerized delivery of genetic counseling services). 3 credits. T, H

HBHE 715 Ethical, Legal, & Social Issues in Genomics and Health - This weekly seminar will address a wide range of ELSI issues involved in the following areas: implementation of genetic screening and testing in medical, public health and direct-to-consumer contexts; ethics of genetics research, including challenges around informed consent, data privacy, and return of individual research results; and legal and policy options for the regulation of genetic testing, genomic research, and precision medicine. This seminar is a requirement for fellows in the NIH-funded University of Michigan ELSI Research Training Program. 1.5 credits. D, H

LHS 671: Ethics and Policy Issues for Learning Health SystemsPolicy and Ethics of Learning Health Systems II --- Policy and ethics shape the landscape and growth of health infrastructures. This course is designed to engage students in policy and ethical inquiry as they learn the theory, technology, and methods integral to health infrastructures and learning systems. Students in this seminar-style course will consider the policy and ethics of research methods, data science, and health infrastructures. 3 credits. D, H

Data Science & Predictive Health Analytics

LHS 650: Data Science and Predictive Analytics - This course aims to build computational abilities, inferential thinking, and practical skills for tackling core data scientific challenges. It explores foundational concepts in data management, processing, statistical computing, and dynamic visualization using modern programming tools and agile web-services. Concept, ideas, and protocols are illustrated through examples of real observational, simulated and research-derived datasets. Some prior quantitative experience in programming, calculus, statistics, mathematical models, or linear algebra will be necessary. 4 credits. D

LHS 853: Scientific Methods for Health Sciences: Special Topics - This course will cover a number of modern analytical methods for advanced healthcare research. Specific focus will be on reviewing and using innovative modeling, computational, analytic and visualization techniques to address specific driving biomedical and healthcare applications. The course will cover the 5 dimensions of Big-Data (volume, complexity, time/scale, source and management). 4 credits. D

LHS 610: Exploratory Data Analysis for Health - Students will learn foundational topics in data science and health information through hands-on work with real health datasets. Students will learn R, one of the most widely used languages for data science. The course contains two large themes: understanding health data and making inferences based on data. 3 credits. D

LHS 712: Natural Language Processing on Health Data - Students will learn advanced methods and techniques in text mining and natural language processing of health-related data, including electronic health records, published literature, and social media. Students will develop computational techniques to analyze different genres of health data, and build resources to search and extract relevant information from free text. 3 credits. D

BIOSTAT 521: Applied Biostatistics - Fundamental statistical concepts related to the practice of public health: descriptive statistics; probability; sampling; statistical distributions; estimation; hypothesis testing; chi-square tests; simple and multiple linear regression; one-way ANOVA. Taught at a more advanced mathematical level than Biostat 503. Use of computer in statistical analysis. 4 credits. D

IOE 574: Simulation Design & Analysis - Discrete event simulation for modeling and analysis. Development of simulations using a high-level programming language. Probabilistic and statistical aspects of simulation, including variate and process generation, variance reduction, and output analysis. Connections to stochastic models and queueing. Applications in services, healthcare, and manufacturing. 3 credits. D

IOE 691: Predictive Analytics for Interdisciplinary Research - The course provides a foundation in how to integrate multiple, diverse data sources to gain strong predictive accuracy and strong insights into challenging, interdisciplinary research problems. The course provides a foundation in semi-parametric and non-parametric predictive modeling, including ensemble-based methods. The focus is on how these models work and how to effectively leverage them for strong data analysis across multiple domains of application. There is a strong focus on model testing and validation, regularization, and implementation of models for prediction. Applications are taken from a variety of fields including risk analysis, operations research, civil engineering, climate science, public health, and disaster science. A major portion of the work in the course is a research-based term project in which students conduct and analysis, aiming for a conference or journal submission. D

BIOSTAT 522: Biostatistical Analysis for Health-Related Studies - A second course in applied biostatistical methods and data analysis. Concepts of data analysis and experimental design for health-related studies. Emphasis on categorical data analysis, multiple regression, analysis of variance and covariance. 3 credits. D

BIOSTAT 650: Applied Statistics I: Linear Regression - Graphical methods, simple and multiple linear regression; simple, partial, and multiple correlation, estimation, hypothesis testing; model building and diagnosis; introduction to nonparametric regression; introduction to smoothing methods (e.g., lowess). The course will include applications to real data. 4 credits. D

STAT 415: Data Mining and Statistical Learning - This course covers the principles of data mining, exploratory analysis and visualization of complex data sets, and predictive modeling. The presentation balances statistical concepts (such as over-fitting data, and interpreting results) and computational issues. Students are exposed to algorithms, computations, and hands-on data analysis in the weekly discussion sessions. 4 credits. D

SI 618: Data Manipulation and Analysis - This course aims to help students get started with their own data harvesting, processing, aggregation, and analysis. Data analysis is crucial to evaluating and designing solutions and applications, as well as understanding user's information needs and use. In many cases the data we need to access is distributed online among many webpages, stored in a database, or available in a large text file. Often these data (e.g. web server logs) are too large to obtain and/or process manually. Instead, we need an automated way of gathering the data, parsing it, and summarizing it, before we can do more advanced analysis. 3 credits. D

SI 670: Applied Machine Learning - Students will learn how to correctly apply, interpret results, and iteratively refine and tune supervised and unsupervised machine learning models to solve a diverse set of problems on real-world datasets. Application is emphasized over theoretical content. 3 credits. D

SI 671: Data Mining: Methods and Applications - This is a seminar course of advanced topics in data mining, the state-of-the-art methods to analyze different genres of information, and the applications to many real world problems. The course will highlight the practical applications of data mining instead of the theoretical foundations of machine learning and statistical computing. The course materials will focus on how the information in different real world problems can be represented as particular genres, or formats of data, and how the basic mining tasks of each genre of data can be accomplished using the state-of-the-art techniques. To this end, the course is suitable for those who are consumers of data mining techniques in their own disciplines, such as natural language processing, networks science, human computer interaction, economics, social computing, sociology, business intelligence, and biomedical informatics, etc. 3 credits. D

Biosocial Determinants of Health/Policy/Economics

HMP 630: Business of Biology - The objective in this interdisciplinary graduate course is to explore the intersections between science, technology, commerce and social policy as they come together to advance (and in some cases retard) progress toward more-personalized health care. The course is intended for graduate students in medicine, biomedical and health-related science, public health, law, engineering, and business interested in the future of health care. 2 credits. H

HMP 513: US Healthcare System - Analysis of current organizational arrangements and patterns for provision and financing of medical care services in United States. Topics include need, access and use of services; issues related to health professionals and health facilities; health care costs; quality assessment and assurance and managed care and health care financing. 3 credits. T, H

PUBHLTH 626: Understanding and Improving the US Healthcare System - Provides as asynchronous, engaging, and interactive way to understand the U.S. healthcare system and gain insight about the system. 1 credit. T, H

Human Genetics in Health and Disease/Molecular Medicine

HUMGEN 541: Molecular Genetics - This course explores how the information content of the DNA genome is (i) organized, propagated, and altered, and (ii) functionally expressed by regulated transcription into RNA - the core molecular properties and processes of genetic systems that underlie all further investigations of organismal, clinical, and population genetics. 3 credits. D

HUMGEN 542: Molecular Basis of Human Genetics in Disease - This course will emphasize the principles and methods of genetics and molecular genetics as they relate to human disease. The course covers the topics of monogenic traits, cytogenetics, non-Mendelian inheritance, cancer genetics, and complex genetic disease. In each section, principles of genetics are presented by way of illustration of particular human genetic diseases or conditions. 3 credits. D

PHAR 647: Clinical Trials for Translational Scientists - In this multidisciplinary course students will design their own clinical trial by being part of a TO-T3 translational research team. Topics covered include trial design, ethical issues, managing the study team, study conduct, IRB and regulatory practice, protecting and respecting participants, managing data and data safety, and communicating findings. 3 credits. D, H

CPTS 820: Clinical Translation in Pharmacokinetics - This course reviews the fundamental and practical aspects of absorption, distribution, metabolism, and excretion (ADME) for therapeutics and helps students strategize, plan and design translational research for drug dose design. 1 credit. D

CPTS 822: Research and Clinical Translation in Pharmacogenomics- This course focuses on methods for research and clinical translation of DNA (genetics and epigenetics) and RNA (transcriptomics) in precision pharmacotherapy, which we globally refer to as "pharmacogenomics". Students will learn research methods such as genomic data generation, analysis, and experimental models. Students will also learn methods for clinical translation such as genomics-driven clinical trials and how pharmacogenetics is currently used in clinical practice. 3 credits. D

BIOMEDE 561: Biological Micro-and Nanotechnology - Many life processes occur at small size-scales. This course covers scaling laws, biological solutions to coping with or taking advantage of small size, micro- and nanofabrication techniques, biochemistry, and biomedical applications (genomics, proteomics, cell biology, diagnostics, etc.). There is an emphasis on microfluidics, surface science, and non-traditional fabrication techniques. 3 credits. D

BIOMEDE 584/CHE 584/MSE 584: Advances in Tissue EngineeringFundamentals engineering and biological principles underlying field of tissue engineering are studied, along with specific examples and strategies to engineering specific tissues for clinical use (e.g., skin). Student design teams propose new approaches to tissue engineering challenges. 3 credits.

BIOMEDE 588/CHE 588: Global Quality Systems and Regulatory Innovation - This course is for scientists, engineers, and clinicians to understand and interpret various relevant global and regional quality systems for traditional and cutting edge global health technologies, solutions and their implementation. Speakers from academia, the FDA, and biomedical related industries will be invited to participate in teaching this course. 2 credits. D

BIOMEDE 599.99: Systems Biology of Human Diseases - This course uses genetic and metabolic design principles to analyze healthy and diseased biological states by working to uncover the metabolic interactions between cancer cells and cells in neighboring tissue that support cancer growth and metastasis. 1-6 credits. D

BIOMEDE 599.011: Engineering Approaches to Cancer Biology - This course aims at designing, building and utilizing new experimental and computational tools to analyze and interpret multi-scale processes that regulate the behavior of human cells and tissues in response to perturbations such as cytokines, stress, cytotoxic and targeted drugs. 1-6 credits. D

BIOMEDE 499.002: Clinical Observation and Needs Finding - In this course, students will observe nurses, technicians, surgeons, and physicians at the UM or VA Hospital, observing clinical practices in various medical specialties and settings. From these observations, students will identify important clinical problems and generate need statements based on their understanding. By the end of the term, students will assess the impact, marketability, and feasibility of solving these needs. 2 credits. D

EPID 515: Genetics in Public Health - This course is designed for students with biology or genetics background, that are interested in understanding genetics in public health. This course will provide an in depth examination of genetics in public health including newborn screening diseases and practices, fundamentals of population genetics, and the genetics of common chronic diseases. 3 credits. D, H

EPID 516: Genetic Epidemiology - This course relates genomics to the core public health discipline of epidemiology emphasizing the use of genomics to help describe disease frequency and distribution and to gain insights into biological etiologies. Topics include genetic material in disease, in families and in populations; the investigation of multifactorial traits; model-based linkage analysis; model-free linkage analysis; segregation analysis; allele association and linkage disequilibrium; and gene-gene interactions and gene-environment interactions. Issues related to implementing studies are considered. 4 credits. D, H

Bioinformatics/Computational Genomics

BIOINF 527: Introduction to Bioinformatics & Computational Biology - This course introduces students to the fundamental theories and practices of Bioinformatics and Computational Biology via a series of integrated lectures and labs. These lectures and labs will focus on the basic knowledge required in this field, methods of high-throughput data generation, accessing public genome-related information and data, and tools for data mining and analysis. The course is divided into four areas: Basics of Bioinformatics, Computational Phylogeny (includes sequence analysis), Systems Biology and Modeling. 4 credits. D

BIOINF 580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences - The course covers signal processing and machine learning methods with an emphasis on their application in healthcare. Students will need a basic understanding in linear algebra for this course. 3 credits. D

BIOSTATS 646/ BIOINF 545: High Throughput Molecular Genetic and Epigenetic Data Analysis - This course will cover basic analysis of microarrays, RNA-Seq, and ChIP-Seq data including hands-on lab sessions. The class also covers an introduction to the underlying biology and the technologies used for measuring RNA levels, transcription factor binding an epigenetic modifications, and quality control of microarray and deep sequencing data. 3 credits. D

Consumer Health Informatics and Healthcare Systems Engineering for Precision Health

IOE 513: Healthcare Operations Research: Theory and Applications - This course provides an overview of the role of operations research in healthcare. It surveys and evaluates research done in this field and addresses some of the key technical issues encountered when developing healthcare operations research models. Insights will be shared about carrying out collaborative research with healthcare professionals. 3 credits. 

LHS 611: Knowledge Representation and Management in Health - Important lessons about how to improve the health of individuals and populations are being learned everyday by a growing number of communities of interest. To apply these lessons broadly, communities of interest need efficient, effective means to represent and manage the new knowledge they generate. Considering the community of interest as a diverse convening, governing, discoursing, learning community made of knowledge generators and users, this course provides an intensive introduction to select social and technical methods to support community needs for representing and managing knowledge. 3 credits. T, H

LHS 621: Implementation Science in Health 1 - Dissemination and implementation sciences are important emerging disciplines. In this course, students learn and apply principles of dissemination and implementation sciences to problems related to healthcare practice and policy, including preparation of an implementation project. This course emphasized the fit between dissemination and implementation sciences and learning health cycles. 3 credits. D, T, H

LHS 650: Health Infrastructures Pro Seminar 1 - Health infrastructures connect networks, of people, organizations, and technologies at multiple levels of scale in physical and virtual spaces to improve the health of individuals and populations. This seminar examines theory and applied case studies to explore infrastructural thinking in the context of learning health system. 3 credits. T, H

IOE 813: Seminars in Healthcare Systems Engineering - Healthcare is critical to society and has a major impact on our economy. In this course, focused around weekly seminars by leading scholars in this important area, we provide a broad overview to ways systems engineering can improve the delivery of healthcare: decreasing costs, reducing error and developing innovations. 2 credits. T, H 

LHS 660: Research Methods for Learning Systems - Foundational introduction to empirical methods, both quantitative and qualitative, applicable to the study of health infrastructures and learning systems. Offers a broad overview that will enable students in the PhD program to begin formulating their interests into researchable problems, and make informed choices of the more advanced research methods courses they will need to pursue their research agenda. 3 credits. D

LHS 721: Implementation Science in Health 2 - Students will apply concepts learned in LHS 621 about how dissemination and implementation sciences fit into the LHS learning cycle, and apply practical skills to implement and evaluate complex interventions to improve health care. Students will complete a project implementing evidence-based practice and prepare reports for multiple audiences. 3 credits. D, T, H 

LHS 750: Health Infrastructures Pro Seminar 2 - Health infrastructures connect networks, of people, organizations, and technologies at multiple levels of scale in physical and virtual spaces to improve the health of individuals and populations. This seminar examines theory and applied case studies to explore infrastructural thinking in the context of learning health system. 2 credits. D, T, H

SI 554/HBHE 654: Consumer Health Informatics - In this course, students will become familiar with a range of consumer health informatics (CHI) applications, including the needs/problems that the applications address, their theoretical bases, and their designs. Building on this prior CHI work, students will acquire an ability to evaluate existing applications, to use design techniques and skills for ideation, and to generate theory-informed design and implementation strategies for CHI applications. Students will also learn to assess the needs and technological practices of potential users, with a particular focus on groups that experience health and information access disparities. 3 credits. 

SI/HBHE 684: Designing Consumer Health Technologies - This course focuses on the design processes, theories, and evaluation methods that can help you construct and iteratively test high-quality consumer health technologies. The course covers prototyping techniques (creation of low and medium-fidelity prototypes, wizard of oz prototyping, and physical prototyping), psychological theories and constructs with direct applicability to consumer health technologies (e.g., behavioral economics, dual process models, operant conditioning), and evaluation techniques (e.g., single-case designs, micro-randomized trials) that can be used to do formative evaluation and optimization of consumer-health technologies. 3 credits. 

SI 669: Developing Mobile Experiences - Develop mobile applications using state of the art tools and platforms. Learn how to use standard testing, monitoring, and debugging tools to find and fix software bugs. Gain familiarity with other mobile app development approaches, UX principles and methods, and emerging mobile technologies such as wearables and Augmented Reality. 3 credits. 

Additional Precision Health Courses 

BIOINF 524/525: Foundations in Bioinformatics - This course provides an introduction to the principles and practical approaches of bioinformatics as applied to genes and proteins. The overall course content is broken down into 3 sections focusing on foundational information, statistics, and systems biology, respectively. 3 credits. D

BIOINF 585: Deep Learning in Bioinformatics - This project-based course is focused on deep learning and advanced machine learning in bioinformatics. The course will be comprised of deep learning and some other traditional machine learning in applications including regulatory genomics, health records, and biomedical images, and computation labs. 4 credits. D

EECS 545: Machine Learning - This course will give a graduate-level introduction of machine learning and provide foundations of machine learning, mathematical derivation and implementation of the algorithms, and their applications. 3 credits. D