Publication Date:
24 February 2020
Journal:
2019 Computing in Cardiology (CinC)
Last Updated:
2021-04-09T13:12:25.233+01:00
Volume:
46
DOI:
10.23919/CinC49843.2019.9005805
abstract:
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.
Symplectic id:
1063244
Submitted to ORA:
Submitted
Publication Type:
Conference Paper
ISBN-13:
9781728159423