Short-term All-cause In-hospital Mortality Prediction by Machine Learning Using Numeric Laboratory Results

Abstract

Introduction: A critical value (or panic value) is a laboratory test result that significantly deviates from the normal value and represents a potentially life-threatening condition requiring immediate action. Although notification of critical values by critical value list (CVL) is a well-established method, their contribution to mortality prediction is unclear. Methods: A total of 335,430 clinical laboratory results from 92,673 patients from July 2018 to December 2019 were used. Data in the first 12 months were divided into two datasets at a ratio of 70:30, and a 7-day mortality prediction model by machine learning (eXtreme Gradient Boosting [XGB] decision tree) was created using stratified random undersampling data of the 70% dataset. Mortality predictions by the CVL and XGB model were validated using the remaining 30% of the data, as well as different 6-month datasets from July to December 2019. Results: < 0.001) in both datasets.The receiver operating characteristic-area under the curve values for the 30% and validation data by XGB were 0.9807 and 0.9646, respectively, which were significantly higher than those by CVL (0.7549 and 0.7172, respectively). Conclusions: Mortality prediction within 7 days by machine learning using numeric laboratory results was significantly better than that by conventional CVL. The results indicate that machine learning enables timely notification to healthcare providers and may be safer than prediction by conventional CVL.

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