Year: 2019
Type: article
Abstract: OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data. MATERIALS AND METHODS: Using an ICU dataset of 13 728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. We perform classification experiments with random forest, gradient boost trees, and support vector machines, using the identified subpopulations to distinguish patients who develop sepsis in the ICU from those who do not. RESULTS: The classification results show that features selected using sepsis subpopulations as background knowledge yield a superior performance in distinguishing septic from non-septic patients regardless of the classification model used. The improved performance is especially pronounced in specificity, which is a current bottleneck in sepsis prediction machine learning models. CONCLUSION: Our findings can steer machine learning efforts toward more personalized models for complex conditions including sepsis.
DOI: 10.1093/jamia/ocz211
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,332
Funders: South London and Maudsley NHS Foundation Trust, Wellcome Trust, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, King's College London, University College London, Public Health Agency, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division, Health Data Research UK
OpenAlex ID: W3000385575