The Signature-Based Model for Early Detection of Sepsis from Electronic Health Records in the Intensive Care Unit
J.L. Morrill, Andrey Kormilitzin, Alejo J. Nevado‐Holgado, Sumanth Swaminathan, Sam Howison, Terry Lyons · Computing in cardiology · 2019
Optimal feature selection leads to enhanced efficiency and accuracy when developing both supervised and unsupervised machine-learning models.In this work, a new signature-based regression model is proposed to automatically identify a patient's risk of sepsis based on physiological data streams and to make a positive or negative prediction of sepsis for every time interval since admission to the intensive care unit.The gradient boosting machine algorithm that uses the features at the current time-points and the signature features extracted from the time-series to model the longitudinal effects of sepsis yields the utility function score of 0.360 (officially ranked 1st, team name: 'Can I get your Signature?') on the full test set.The signature method shows a systematic and competitive approach to model sepsis by learning from health data streams.