Titlebar-MIT EECS  
 

Biomedical Signal Processing & Imaging
Clinical Decision Making: Automated detection of pulsus paradoxus
Computational Biophysics
Computational Genomics & Proteomics
Computational Neuroscience
Micro/Nanotechnology for Biology & Medicine
Quantitative Physiology
Sensory Communication
Synthetic Biology
Tissue Engineering

Clinical Decision Making

Courses: 6.872J, 6.873J, HST.947

John Guttag
Thomas Heldt
Tomas Lozano-Perez
Roger Mark

Peter Szolovits
George Verghese


Medical practice has traditionally employed a combination of scientific modeling and the healing arts. The goal of our activities in clinical decision making is to better understand diagnostic, prognostic and therapeutic reasoning and to develop engineering models that detect and prevent errors, suggest better decisions, educate patients and providers, and smooth the flow of information useful in health care. This leads to projects spanning a rich and diverse set of technical and applied aims, including real-time tracking of patients and their medical status, improved methods of representing and reasoning with clinical knowledge and data, and exploiting the relationship between clinical data and genomic and proteomic data to explicate the mechanisms of disease and to improve its treatment.

 

 

 


BioEECS Home Page | EECS Home Page | Site Map | Search | About this page | Comments and inquiries welcome