Hernández-Arango Alejandro, Arias María Isabel, Pérez Viviana, Chavarría Luis Daniel, Jaimes Fabian
Department of Internal Medicine, University of Antioquia, Medellín, Colombia.
Hospital Alma Mater de Antioquia, University of Antioquia, Medellín, Colombia.
J Med Syst. 2025 Feb 3;49(1):19. doi: 10.1007/s10916-025-02140-z.
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. This cohort study evaluated three machine learning algorithms-XGBoost, Elastic Net logistic regression, and an Artificial Neural Network-to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. 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 mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848-0.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865-0.927), and the Neural Network achieved 0.886 (95% CI: 0.853-0.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937-0.965), the XGBoost model achieved 0.963 (95% CI: 0.952-0.974), and the Neural Network scored 0.932 (95% CI: 0.915-0.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971-0.987) for Elastic Net, 0.977 (95% CI: 0.967-0.986) for XGBoost, and 0.976 (95% CI: 0.968-0.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making.
由使用基于人工智能的包含多个变量的模型的临床决策支持系统指导的慢性病决策，需要在不同人群中进行科学验证，以优化全球医疗系统中有限的人力、财力和临床资源的使用。这项队列研究评估了三种机器学习算法——极端梯度提升（XGBoost）、弹性网络逻辑回归和人工神经网络——来开发针对三种结果的预测模型：死亡率、住院率和急诊就诊率。目的是为哥伦比亚麦德林阿尔马 Mater 医院综合院区治疗的非传染性疾病患者建立一个临床决策支持系统。我们从纳入研究的 5000 名患者中收集了 4845 份电子病历记录。中位年龄为 71.83 岁，女性占 63.8%，29.7%接受家庭护理。最常见的医疗状况是糖尿病（52.9%）、高血压（67.2%）、血脂异常（57.3%）和慢性阻塞性肺疾病（19.4%）。对于死亡率预测，弹性网络逻辑回归模型的曲线下面积（AUCROC）为 0.883（95%置信区间：0.848 - 0.917），极端梯度提升（XGBoost）模型达到 0.896（95%置信区间：0.865 - 0.927），神经网络为 0.886（95%置信区间：0.853 - 0.9
