Universidad Politécnica de Madrid, Av. Complutense, 30, 28040, Madrid, Spain.
Int J Med Inform. 2023 Oct;178:105172. doi: 10.1016/j.ijmedinf.2023.105172. Epub 2023 Aug 8.
Frailty in older people is a syndrome related to aging that is becoming increasingly common and problematic as the average age of the world population increases. Detecting frailty in its early stages or, even better, predicting its appearance can greatly benefit health in later years of life and save the healthcare system from high costs. Machine Learning models fit the need to develop a tool for supporting medical decision-making in detecting or predicting frailty.
In this review, we followed the PRISMA methodology to conduct a systematic search of the most relevant Machine Learning models that have been developed so far in the context of frailty. We selected 41 publications and compared them according to their purpose, the type of dataset used, the target variables, and the results they obtained, highlighting their shortcomings and strengths.
The variety of frailty definitions allows many problems to fall into this field, and it is often challenging to compare results due to the differences in target variables. The data types can be divided into gait data, usually collected with sensors, and medical records, often in the context of aging studies. The most common algorithms are well-known models available from every Machine Learning library. Only one study developed a new framework for frailty classification, and only two considered Explainability.
This review highlights some gaps in the field of Machine Learning applied to the assessment and prediction of frailty, such as the need for a universal quantitative definition. It emphasizes the need for close collaboration between medical professionals and data scientists to unlock the potential of data collected in hospital and clinical settings. As a suggestion for future work, the area of Explainability, which is crucial for models in medicine and health care, was considered in very few studies.
老年人衰弱是一种与衰老相关的综合征，随着世界人口平均年龄的增长，衰弱的发病率越来越高，问题也越来越多。早期发现衰弱，或者更好的是，预测其出现，可以极大地促进老年人的健康，并为医疗保健系统节省高额成本。机器学习模型符合开发一种支持医学决策的工具的需求，以检测或预测衰弱。
在这项综述中，我们遵循 PRISMA 方法学，对迄今为止为衰弱而开发的最相关的机器学习模型进行了系统搜索。我们选择了 41 篇出版物，并根据它们的目的、使用的数据集类型、目标变量以及它们获得的结果进行了比较，突出了它们的优缺点。
衰弱定义的多样性使得许多问题都属于这个领域，由于目标变量的差异，往往难以比较结果。数据类型可以分为步态数据，通常使用传感器收集，以及医疗记录，通常在老龄化研究的背景下。最常见的算法是每个机器学习库都有的知名模型。只有一项研究为衰弱分类开发了一个新的框架，只有两项研究考虑了可解释性。
这项综述突出了机器学习在评估和预测衰弱方面的一些空白，例如需要一个通用的定量定义。它强调了医疗专业人员和数据科学家之间密切合作的必要性，以挖掘医院和临床环境中收集的数据的潜力。作为对未来工作的建议，考虑到了模型在医学和保健中的重要性，可解释性领域在很少的研究中被考虑。
