Purpose: The aging population is leading to a rise in the number of patients with multiple chronic conditions (MCC), which is putting pressure on healthcare systems. Artificial Intelligence, including Machine Learning (ML) offers potential to enhance care for patients with MCC. This scoping review summarizes current ML applications, discusses shortcomings and identifies opportunities. Additionally, it aims to identify applications explored in practice. Methods: We searched PubMed, Embase, and Web of Science for studies published between 2015 and January 2025 that used ML techniques and specialist care data, focusing on adults with MCC. Screening was assisted by ASReview. Results: The search identified 13381 articles, of which 454 were reviewed full text, resulting in 21 included articles. ML was mainly used for clustering (n = 14), primarily focusing on cardiovasculair diseases, with eight studies focusing on chronic diseases and six studies on clinical features, like medical specialties involved and symptoms. Stated potential clinical use of the clusters varied, but primarily aimed to promote integrated, personalized care. Predictive modelling was employed to support clinical decision-making and enhance research (n = 7). No applications were clinically evaluated. Conclusion: Current research on ML for patients with MCC primarily focuses on cluster analysis and predictive modelling, mainly aiming to enable holistic care. Future efforts should explore clinical evaluation and implementation, Natural Language Processing and Large Language Models. These technologies could significantly enhance care by extracting valuable insights from the data-rich electronic patient records of MCC patients, potentially leading to more effective decision-making and tailored interventions.

Background and purpose — Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven prediction models for short-and long-term mortality in a large cohort of patients with hip fractures. Methods — In this national registry-based retrospective cohort study, patients aged ≥ 70 years registered in the nationwide Dutch Hip Fracture Audit from 2018–2023 were included. Predictive variables were selected based on the literature and/or clinical relevance. 6 ML algorithms, including logistic regression, were trained with internal cross-validation and evaluated on discrimination (c-statistic), sensitivity, specificity, calibration, and interpretability. Results — 74,396 patients (median age 84, IQR 78–89; 68% female) were analyzed. Most patients lived at home (69%) and high malnutrition risk was seen in 10%. 18% had dementia. Mortality rates were 9.1% (30-day), 15% (90-day), and 26% (1-year). Logistic regression performed comparably to other algorithms, but was chosen as the preferred algorithm due to its superior interpretability (c-statistic: 30-day 0.82, 90-day 0.81, 1-year 0.80). Conclusion — We developed and validated ML algorithms, including logistic regression, for mortality prediction in older hip fracture patients with adequate performance. This information may inform SDM.