Prediction of the risk of adverse clinical outcomes with machine learning techniques in patients with chronic no communicable diseases

Abstract

<title>Abstract</title> Background Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. Methods In this cohort study, a prediction model was derived by evaluating two algorithms, XGBoost and Elastic Net logistic regression, for three outcomes - mortality, hospitalization, and emergency department visits - to build a clinical decision support system for patients with non-communicable chronic diseases at the Alma Mater Hospital complex in Medellin, Colombia. Results We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For the mortality outcome, the Elastic Net logistic regression model had an AUCROC of 0.88 (95% CI, 0.8032 to 0.9032), and the XGBoost model had an AUCROC of 0.912 (95% CI, 0.8802 to 0.9437). For the hospitalization outcome, the Elastic Net logistic regression model had an AUCROC of 0.967 (95% CI, 0.957 to 0.9763), while the XGBoost model had an AUCROC of 0.976 (95% CI, 0.9661 to 0.985). For the emergency department visit outcome, the Elastic Net logistic regression model had an AUCROC of 0.930 (95% CI, 0.9158 to 0.945), while the XGBoost model had an AUCROC of 0.982 (95% CI, 0.9755 to 0.9891). We created a dashboard as to interact with the model, segmenting risk in the cohort. Conclusions A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with chronic diseases for effective decision-making.

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