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Using machine learning to predict severe hypoglycaemia in hospital
Author(s) -
Fralick Michael,
Dai David,
PouProm Chloe,
Verma Amol A.,
Mamdani Muhammad
Publication year - 2021
Publication title -
diabetes, obesity and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.445
H-Index - 128
eISSN - 1463-1326
pISSN - 1462-8902
DOI - 10.1111/dom.14472
Subject(s) - decile , logistic regression , medicine , lasso (programming language) , machine learning , artificial intelligence , retrospective cohort study , cohort , psychological intervention , emergency medicine , computer science , statistics , mathematics , nursing , world wide web
Aim To predict the risk of hypoglycaemia using machine‐learning techniques in hospitalized patients. Methods We conducted a retrospective cohort study of patients hospitalized under general internal medicine (GIM) and cardiovascular surgery (CV) at a tertiary care teaching hospital in Toronto, Ontario. Three models were generated using supervised machine learning: least absolute shrinkage and selection operator (LASSO) logistic regression; gradient‐boosted trees; and a recurrent neural network. Each model included baseline patient data and time‐varying data. Natural‐language processing was used to incorporate text data from physician and nursing notes. Results We included 8492 GIM admissions and 8044 CV admissions. Hypoglycaemia occurred in 16% of GIM admissions and 13% of CV admissions. The area under the curve for the models in the held‐out validation set was approximately 0.80 on the GIM ward and 0.82 on the CV ward. When the threshold for hypoglycaemia was lowered to 2.9 mmol/L (52 mg/dL), similar results were observed. Among the patients at the highest decile of risk, the positive predictive value was approximately 50% and the sensitivity was 99%. Conclusion Machine‐learning approaches can accurately identify patients at high risk of hypoglycaemia in hospital. Future work will involve evaluating whether implementing this model with targeted clinical interventions can improve clinical outcomes.