Olender Robert T, Roy Sandipan, Nishtala Prasad S
Department of Pharmacy and Pharmacology, University of Bath, Claverton Down, Bath BA2 7AY, UK.
Department of Mathematical Sciences, University of Bath, Bath, UK.
Age Ageing. 2025 May 31;54(6). doi: 10.1093/ageing/afaf156.
Machine learning (ML) models in healthcare are crucial for predicting clinical outcomes, and their effectiveness can be significantly enhanced through improvements in accuracy, generalisability, and interpretability. To achieve widespread adoption in clinical practice, risk factors identified by these models must be validated in diverse populations.
In this cohort study, 86 870 community-dwelling older adults ≥65 years from the UK Biobank database were used to train and test three ML models to predict 30-day emergency hospitalisation. The three ML models, Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR), utilised all extracted variables, consisting of demographic and geriatric syndromes, comorbidities, and the Drug Burden Index (DBI), a measure of potentially inappropriate polypharmacy, which quantifies exposure to medications with anticholinergic and sedative properties. 30-day emergency hospitalisation was defined as any hospitalisation related to any clinical event within 30 days of the index date. The model performance metrics included the area under the receiver operating characteristics curve (AUC-ROC) and the F1 score.
The AUC-ROC for the RF, XGB and LR models was 0.78, 0.86 and 0.61, respectively, signifying good discriminatory power. The DBI, mobility, fractures, falls, hazardous alcohol drinking and smoking were validated as important variables in predicting 30-day emergency hospitalisation.
This study validated important risk factors for predicting 30-day emergency hospitalisation. The validation of important risk factors will inform the development of future ML studies in geriatrics. Future research should prioritise the development of targeted interventions to address the risk factors validated in this study, ultimately improving patient outcomes and alleviating healthcare burdens.
医疗保健领域的机器学习（ML）模型对于预测临床结果至关重要，通过提高准确性、通用性和可解释性，其有效性可得到显著提升。为了在临床实践中广泛应用，这些模型识别出的风险因素必须在不同人群中得到验证。
在这项队列研究中，来自英国生物银行数据库的86870名年龄≥65岁的社区居住老年人被用于训练和测试三个ML模型，以预测30天内的急诊住院情况。这三个ML模型，即随机森林（RF）、极端梯度提升（XGB）和逻辑回归（LR），使用了所有提取的变量，包括人口统计学和老年综合征、合并症以及药物负担指数（DBI），这是一种衡量潜在不适当多重用药的指标，用于量化接触具有抗胆碱能和镇静特性药物的情况。30天内的急诊住院被定义为在索引日期后30天内与任何临床事件相关的任何住院情况。模型性能指标包括受试者工作特征曲线下面积（AUC-ROC）和F1分数。
RF、XGB和LR模型对应的AUC-ROC分别为0.78、0.86和0.61，表明具有良好的区分能力。DBI、行动能力、骨折、跌倒、有害饮酒和吸烟被验证为预测30天急诊住院的重要变量。
本研究验证了预测30天急诊住院的重要风险因素。重要风险因素的验证将为未来老年医学领域的ML研究发展提供参考。未来的研究应优先开发针对性干预措施，以解决本研究中验证的风险因素，最终改善患者预后并减轻医疗负担。
