Machine Learning–Based Mortality Prediction of Patients at Risk During Hospital Admission

Abstract

Objectives:The ability to predict in-hospital mortality from data available at hospital admission would identify patients at risk and thereby assist hospital-wide patient safety initiatives. Our aim was to use modern machine learning tools to predict in-hospital mortality from standardized data sets available at hospital admission.Methods: This was a retrospective, observational study in 3 adult tertiary care hospitals in Western Australia between January 2008 and June 2017. Primary outcome measures were the area under the curve for the receiver operating characteristics curve, the F 1 score, and the average precision of the 4 machine learning algorithms used: logistic regression, neural networks, random forests, and gradient boosting trees.Results: Using our 4 predictive models, in-hospital mortality could be predicted satisfactorily (areas under the curve for neural networks, logistic regression, random forests, and gradient boosting trees: 0.932, 0.936, 0.935, and 0.935, respectively), with moderate F 1 scores: 0.378, 0.367, 0.380, and 0.380, respectively. Average precision values were 0.312, 0.321, 0.334, and 0.323, respectively. It remains unknown whether additional features might improve our models; however, this would result in additional efforts for data acquisition in daily clinical practice.Conclusions: This study demonstrates that using only a limited, standardized data set in-hospital mortality can be predicted satisfactorily at the time point of hospital admission. More parameters describing patient's health are likely needed to improve our model.

Prospective and External Validation of Prognostic Machine Learning Models for Short- and Long-Term Mortality Among Acutely Admitted Patients Based on Blood Tests.

“…conducted a study analyzing data from three adult tertiary care hospitals in Australia 19 . This study achieved a remarkable AUC of 0.93 for predicting in-hospital mortality among all admitted patients, regardless of whether their cases were medical or surgical.…”

Potential Predictors for Deterioration of Renal Function After Transfusion

“…ML may also be able to model sparse data and provide guidance. 11,12 Therefore, we used ML to predict AKI as the primary outcome, a typical side effect of blood transfusions. We hypothesize that ML can predict this transfusion-associated renal complication using features obtained from the recipient and features connected to the donor and the corresponding unit of RBCs.…”