{"title":"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。","authors":"Chen Bai, Mamoun T. Mardini","doi":"10.1016/j.arr.2024.102529","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"101 ","pages":"Article 102529"},"PeriodicalIF":12.5000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advances of artificial intelligence in predicting frailty using real-world data: A scoping review\",\"authors\":\"Chen Bai, Mamoun T. Mardini\",\"doi\":\"10.1016/j.arr.2024.102529\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>\",\"PeriodicalId\":55545,\"journal\":{\"name\":\"Ageing Research Reviews\",\"volume\":\"101 \",\"pages\":\"Article 102529\"},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2024-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ageing Research Reviews\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1568163724003477\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ageing Research Reviews","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568163724003477","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Advances of artificial intelligence in predicting frailty using real-world data: A scoping review
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.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
