The criteria for study inclusion.

Aim
In this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.
Methods
We searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.
Results
We screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance.
Conclusion
This review has identified considerable concerns regarding methodological issues and reporting quality in studies investigating ML to predict hospitalizations. To ensure the acceptability of these models in clinical settings, it is crucial to improve the quality of future studies.

研究目的 本综述旨在探讨机器学习（Machine Learning，ML）如何被应用于成人全因躯体性住院收治与再入院的预测任务。
研究方法 本研究从各数据库建库至2023年10月，检索了PubMed、Embase、Web of Science、CINAHL、ProQuest、OpenGrey、WorldCat及MedNar共8个数据库，纳入采用机器学习方法预测成人全因躯体性住院收治与再入院的相关文献。数据提取采用CHARMS清单完成，偏倚与适用性评估使用PROBAST工具，报告质量评价则依据TRIPOD标准进行。
研究结果 本研究共筛选出7543项研究，其中通读163篇全文文献，最终有116篇符合本综述的纳入标准。其中45项研究针对住院收治情况进行预测，70项聚焦于再入院预测，另有1项研究同时涵盖两类预测任务。相关研究在数据集类型、算法选型、特征工程、数据预处理流程、评估与验证方法等方面存在显著异质性。最常使用的特征类型包括人口统计学信息、诊断结果、生命体征及实验室检测指标。受试者工作特征曲线下面积（Area Under the ROC Curve，AUC）是最主流的评估指标。基于提升树类算法训练的模型整体表现优于其他算法，机器学习算法也普遍优于传统回归技术。共有16项研究采用临床笔记的自然语言处理（Natural Language Processing，NLP）开展预测任务，所有相关研究均取得了良好的预测效果。纳入本综述的研究整体报告质量不佳，仅5%的模型实现了临床落地应用。最常未得到充分处理的方法学要点包括：提供个体患者层面的模型解释、完整代码开源、开展外部验证、模型校准以及处理类别不平衡问题。
研究结论 本综述发现，在探索机器学习用于住院情况预测的相关研究中，仍存在较多方法学缺陷与报告质量不足的问题。为确保此类模型能够被临床场景所接纳，提升未来相关研究的整体质量至关重要。