Predicting whether patients in an acute medical unit are physiologically fit-for-discharge using machine learning: A
S H Garssen1, C A Vernooij2, N Kant3
1Health Technology and Services Research, Techmed Centre, Faculty of Behavioural, Management and Social Sciences, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; Clinical Research Center, Rijnstate Hospital, Wagnerlaan 55, 6815 AD Arnhem, The Netherlands; Department of Patient Care and Monitoring, Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands.
Introduction:
Delays in discharging patients from Acute Medical Units hamper patient flows throughout the hospital. The decision to discharge a patient is mainly based on the patients' physiological condition, but may vary between physicians. An objective decision-support system based on patients' physiological data may help minimizing unnecessary delays in discharge. The aim of this proof-of-concept study is to assess the feasibility of predicting whether patients in an Acute Medical Unit are physiologically fit-for-discharge using machine learning with commonly available hospital data. Furthermore, this study investigated how long before actual time of discharge from the Acute Medical Unit we could predict discharge fitness. Also, the predictive importance of features extracted from these data was assessed.
Methods:
Electronic Medical Records of patients who participated in a Randomized Controlled Trial conducted in an Acute Medical Unit were used retrospectively (N = 199). Only commonly available hospital data were used. Logistic Regression and Random Forest models were applied to predict every hour whether patients were physiologically fit-for-discharge. Nested 5-fold cross-validation with 5 repeats was used to optimize the model hyperparameters and to estimate the predictive performances.
Results:
Physiological discharge fitness was predictable with reasonable performance for Logistic Regression (mean AUROC: 0.67) and Random Forest (mean AUROC: 0.69). For an intuitively chosen classification threshold of 0.8, mean specificity was 93.3 % and sensitivity 14.1 %. Models could predict physiological discharge fitness more than 24 h earlier than actual time of discharge for most patients who were correctly predicted to be fit-for-discharge. Patient characteristics, vital signs and laboratory results were shown to be important predictors.
Conclusion:
This proof-of-concept study showed that it is feasible to predict with machine learning whether patients in an Acute Medical Unit are physiologically fit-for-discharge using commonly available hospital data.
