ICU Patient State Characterization Using Machine Learning in a Time Series Framework

Abstract

We present a methodology for the study of real-world time-series data using supervised machine learning techniques. It is based on the windowed construction of dynamic explanatory models, whose evolution over time points to state changes. It has been developed to suit the needs of data monitoring in adult Intensive Care Unit, where data are highly heterogeneous. Changes in the built model are considered to reflect the underlying system state transitions, whether of intrinsic or exogenous origin. We apply this methodology after making choices based on field knowledge and ex-post corroborated assumptions. The results appear promising, although an extensive validation should be performed.

Available variables include respiratory and hæmodynamic parameters, ventilator settings, blood gas measurements — these constitute our observation variables. (As of October 1998, in the about 200 patient-days in the AidDiag database, the median number of parameters recorded per session was 22.)