Early Detection of Sepsis using Ensemblers
This addresses sepsis detection for medical applications, but it appears incremental as it builds on existing challenge data and methods.
The paper tackled early detection of sepsis by analyzing hourly patient records from the Physionet 2019 challenge, achieving an accuracy of 93.45% and a utility score of 0.271.
This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accuracy of 93.45% and a utility score of 0.271. The utility score as defined by the organizers takes into account true positives, negatives and false alarms.