首页|University of Florida Reports Findings in Artificial Intelligence (Advances of a rtificial intelligence in predicting frailty using real-world data: A scoping re view)
University of Florida Reports Findings in Artificial Intelligence (Advances of a rtificial intelligence in predicting frailty using real-world data: A scoping re view)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news originating from Gainesville, Fl orida, by NewsRx correspondents, research stated, “Frailty assessment is imperat ive for tailoring healthcare interventions for older adults, but its implementat ion remains challenging due to the effort and time needed. The advances of artif icial intelligence (AI) and natural language processing (NLP) present a novel op portunity to harness real-world data (RWD) including electronic health records, administrative claims, and other routinely collected medical records for frailty assessments.” Our news journalists obtained a quote from the research from the University of F lorida, “We followed the PRISMA-ScR guideline and searched Embase, Web of Scienc e, and PubMed databases for articles that predict frailty using AI through RWD f rom inception until October 2023. We synthesized and analyzed the selected publi cations according to their field of application, methodologies employed, validat ion processes, outcomes achieved, and their respective limitations and strengths . 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 categor ized into two groups based on the type of data utilized: 1) AI models using stru ctured data and 2) NLP techniques applied to unstructured clinical notes. We fou nd that AI models achieved moderate to high predictive performance in predicting frailty. However, to demonstrate their clinical utility, these models require f urther validation using external data and a comprehensive assessment of their im pact on patients’ health outcomes. Additionally, the application of NLP in frail ty prediction is still in its early stages. Great potential exists to enhance fr ailty prediction by integrating structured data and clinical notes. The combinat ion of AI and RWD presents significant opportunities for advancing frailty asses sment.”
GainesvilleFloridaUnited StatesNor th and Central AmericaArtificial IntelligenceEmerging TechnologiesMachine Learning
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