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Physiological Patient Monitoring Using Switching State Space Approaches


Term:

Fall

Department:

QI: MIT Quest for Intelligence

Faculty Supervisor:

Li-wei Lehman

Faculty email:

lilehman@mit.edu

Apply by:

8/1/2020

Contact:

http://web.mit.edu/lilehman/www/

Project Description

Switching state-space approaches, such as switching vector autoregressive processes, and switching linear dynamical systems, model complex dynamical phenomena as repeated returns to a set of simpler linear dynamic systems. This project aims to apply switching state space approaches to jointly model time-varying changes in multivariate physiological time series of a patient cohort for informed clinical treatment decision making. More specifically, this project will involve applying switching state space techniques to model real-world physiological time series of a patient cohort, using the learned switching dynamics to characterize the changing physiological states of patients, and analyzing the learned time-varying dynamics in the context of clinical treatment and outcomes. Related Works (see http://web.mit.edu/lilehman/www/) "A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction", Li-wei Lehman et al., IEEE JBHI 2014. "Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring", Li-wei Lehman et al., Chapter in Advanced State Space Methods for Neural and Clinical Data, Cambridge University Press, 2015.

Pre-requisites

The candidate should have experience in machine learning. Knowledge and experience in one or more of the following areas would be desirable: signal processing, probabilistic graphical models, state space models, dynamical systems, and deep learning.