A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation. (March 2020)

• Record Type: Journal Article
• Authors: Faisal, Muhammad; Scally, Andy; Howes, Robin; Beatson, Kevin; Richardson, Donald; Mohammed, Mohammed A
• Other Names: Bian Jiang guest-editor.; Modave Francois guest-editor.
• Is Part Of: Health informatics journal. Volume 26:Number 1(2020)
• Page Start: 34
• Page End: 44
• Publication Date: 2020-03
• Subjects: classification and prediction -- computationally intensive methods -- databases and data mining -- electronic health records -- modelling healthcare services -- statistical modelling; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458218813600
• Languages: English
• ISSNs: 1460-4582

Abstract

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients' first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24, 696) and compared the performance of these models in data from another hospital ( n = 13, 477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.