Background: Frail individuals are very vulnerable to stressors, which often lead to adverse outcomes. To ensure an adequate therapy, a holistic diagnostic approach is needed which is provided in geriatric wards. It is important to identify frail individuals outside the geriatric ward as well to ensure that they also benefit from the holistic approach.
Objectives: The goal of this study was to develop a machine learning model to identify frail individuals in hospitals. The model should be applicable without additional effort, quickly and in many different places in the healthcare system.
Methods: We used Gradient Boosting Decision Trees (GBDT) to predict a frailty target derived from a gold standard assessment. The used features were laboratory values, age and sex. We also identified the most important features.
Results: The best GBDT achieved an AUROC of 0.696. The most important laboratory values are urea, creatinine, granulocytes, chloride and calcium.
Conclusion: The model performance is acceptable, but insufficient for clinical use. Additional laboratory values or the laboratory history could improve the performance.
Keywords: Diagnostic Screening Programs; Frailty; Hospital Laboratory; Machine Learning.