printer friendly pageBIOMEDIN 219: Mathematical Models and Medical Decisions
Analytic methods for determining the optimal diagnostic and therapeutic decisions for the care of individual patients and for the design of policies affecting the care of patient populations. Topics: utility theory and probability modeling, empirical methods for estimating disease prevalence, probability models for periodic processes, binary decision-making techniques, Markov models of dynamic disease state problems, utility assessment techniques, parametric utility models, utility models for multidimensional outcomes, analysis of time-varying clinical outcomes, and the design of cost-contstrained clinical policies. Extensive problem sets compliment course materials. Prerequisites: introduction to calculus and basic statistics.
Terms: Win
| Units: 2
Instructors:
Higgins, M. (PI)
;
Musen, M. (SI)
BIOS 219: Human Gene Regulation: Genomic Thinking and Genomic Tools for Experimentalists
Focused look at the promoter/enhancer and related landscape of the human genome. Genomics and epigenomics of human gene regulation - truth, myths and mysteries. Genomic tools for the interpretation of vertebrate gene regulation experiments and predictions, and the insights behind them. Genomic thinking: purity vs. comprehensiveness, genome-wide vs. single locus. Prerequisites: undergraduate Biology or equivalent. Programming skills not required or taught.
Last offered: Spring 2014
FAMMED 219: Mind-Body Medicine
A small group (8-10) of medical students experientially exploring of the interconnections among human capacities such as thought, emotion, belief, attitudes, and physical health. Review and practice of specific skills (including mindfulness exercises, meditation, imagery, visualization, body awareness, autogenics, and biofeedback) to enhance self-awareness, self-expression, and stress management. Readings relevant to mind-body medicine made available. Anticipated benefits to class participants include discovering and mobilizing their capacity to participate in valuable and proven methods of self knowledge and stress reduction, while dealing with the frustrations and alienation that many students experience in medical school and beyond.
Terms: Win, Spr
| Units: 1
Instructors:
Weinlander, E. (PI)
HRP 219: Evaluating Technologies for Diagnosis, Prediction and Screening
New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state. Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessments and original methods papers are used. Software used in the course is R or Stata. Open to graduate students with a solid understanding of introductory biostatistics, epidemiologic and clinical research study design, and of medical conditions and related technologies required. Basic understanding of Stata or R is also required. Undergraduates may enroll with consent of instructor.
Terms: Win
| Units: 3
Instructors:
Goodman, S. (PI)
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