The signature-based model for early detection of sepsis from electronic health records in the intensive care unit

Conference item

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.

Authors: Morrill, J; Kormilitzin, A; Nevado-Holgado, A; Swaminathan, S; Howison, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2019 Computing in Cardiology (CinC)
• Volume: 46
• Publication date: 2020-02-24
• Acceptance date: 2019-10-01
• Event title: 2019 Computing in Cardiology (CinC)
• Event location: Singapore
• Event website: http://cinc2019.org/
• Event start date: 2019-09-08
• Event end date: 2019-09-11
• DOI: 10.23919/CinC49843.2019.9005805
• EISSN: 2325-887X
• ISSN: 2325-8861
• EISBN: 9781728169361
• ISBN: 9781728159423
• Language: English
• Keywords: boosting; predictive models; feature extraction; training; lead; FFR; Microsoft Windows