Advances of artificial intelligence in predicting frailty using real-world data: A scoping review

IF 12.5 1区医学 Q1 CELL BIOLOGY

Ageing Research Reviews Pub Date : 2024-10-05 DOI:10.1016/j.arr.2024.102529

Chen Bai, Mamoun T. Mardini

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引用次数: 0

Abstract

Background

Frailty assessment is imperative for tailoring healthcare interventions for older adults, but its implementation remains challenging due to the effort and time needed. The advances of artificial intelligence (AI) and natural language processing (NLP) present a novel opportunity to harness real-world data (RWD) including electronic health records, administrative claims, and other routinely collected medical records for frailty assessments.

Methods

We followed the PRISMA-ScR guideline and searched Embase, Web of Science, and PubMed databases for articles that predict frailty using AI through RWD from inception until October 2023. We synthesized and analyzed the selected publications according to their field of application, methodologies employed, validation processes, outcomes achieved, and their respective limitations and strengths.

Results

A total of 23 publications were selected from the initial search (N=2067) and bibliography. The approaches to frailty prediction using RWD and AI were categorized into two groups based on the type of data utilized: 1) AI models using structured data and 2) NLP techniques applied to unstructured clinical notes. We found that AI models achieved moderate to high predictive performance in predicting frailty. However, to demonstrate their clinical utility, these models require further validation using external data and a comprehensive assessment of their impact on patients’ health outcomes. Additionally, the application of NLP in frailty prediction is still in its early stages. Great potential exists to enhance frailty prediction by integrating structured data and clinical notes.

Conclusion

The combination of AI and RWD presents significant opportunities for advancing frailty assessment. To maximize the advantages of these technological advances, future research is needed to rigorously address the challenges associated with the validation of AI models and innovative data integration.

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人工智能在利用真实世界数据预测虚弱方面的进展：范围综述。

背景：虚弱程度评估是为老年人量身定制医疗干预措施的当务之急，但由于需要花费大量精力和时间，其实施仍具有挑战性。人工智能（AI）和自然语言处理（NLP）的发展为利用真实世界数据（RWD）（包括电子健康记录、行政索赔和其他常规收集的医疗记录）进行虚弱程度评估提供了新的机遇：我们遵循 PRISMA-ScR 指南，在 Embase、Web of Science 和 PubMed 数据库中搜索了从开始到 2023 年 10 月通过 RWD 使用人工智能预测虚弱程度的文章。我们根据所选出版物的应用领域、采用的方法、验证过程、取得的成果以及各自的局限性和优势对其进行了综合分析：结果：从初步检索（N=2067）和参考书目中共筛选出 23 篇出版物。根据所使用的数据类型，将使用 RWD 和人工智能进行虚弱预测的方法分为两类：1）使用结构化数据的人工智能模型；2）应用于非结构化临床笔记的 NLP 技术。我们发现，人工智能模型在预测虚弱方面取得了中等至高等的预测效果。不过，要证明这些模型的临床实用性，还需要使用外部数据对其进行进一步验证，并全面评估其对患者健康结果的影响。此外，NLP 在虚弱预测中的应用仍处于早期阶段。通过整合结构化数据和临床笔记，增强虚弱预测的潜力巨大：结论：人工智能和 RWD 的结合为推进虚弱评估带来了重大机遇。为了最大限度地发挥这些技术进步的优势，未来的研究需要严格解决与人工智能模型验证和创新数据整合相关的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ageing Research Reviews 医学-老年医学

CiteScore

19.80

自引率

2.30%

发文量

216

审稿时长

55 days

期刊介绍： With the rise in average human life expectancy, the impact of ageing and age-related diseases on our society has become increasingly significant. Ageing research is now a focal point for numerous laboratories, encompassing leaders in genetics, molecular and cellular biology, biochemistry, and behavior. Ageing Research Reviews (ARR) serves as a cornerstone in this field, addressing emerging trends. ARR aims to fill a substantial gap by providing critical reviews and viewpoints on evolving discoveries concerning the mechanisms of ageing and age-related diseases. The rapid progress in understanding the mechanisms controlling cellular proliferation, differentiation, and survival is unveiling new insights into the regulation of ageing. From telomerase to stem cells, and from energy to oxyradical metabolism, we are witnessing an exciting era in the multidisciplinary field of ageing research. The journal explores the cellular and molecular foundations of interventions that extend lifespan, such as caloric restriction. It identifies the underpinnings of manipulations that extend lifespan, shedding light on novel approaches for preventing age-related diseases. ARR publishes articles on focused topics selected from the expansive field of ageing research, with a particular emphasis on the cellular and molecular mechanisms of the aging process. This includes age-related diseases like cancer, cardiovascular disease, diabetes, and neurodegenerative disorders. The journal also covers applications of basic ageing research to lifespan extension and disease prevention, offering a comprehensive platform for advancing our understanding of this critical field.