Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Accounts of Chemical Research Pub Date : 2023-06-01 Epub Date: 2023-04-06 DOI:10.1097/SPC.0000000000000645
Varun Reddy, Abdulwadud Nafees, Srinivas Raman
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引用次数: 1

Abstract

Purpose of review: Artificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the recent studies applying AI to SPC in cancer patients.

Recent findings: Between 2020 and 2022, 29 relevant studies were identified and categorized into two applications: predictive modeling and text screening. Predictive modeling uses machine learning and/or deep learning algorithms to make predictions regarding clinical outcomes. Most studies focused on predicting short-term mortality risk or survival within 6 months, while others used models to predict complications in patients receiving treatment and forecast the need for SPC services. Text screening typically uses natural language processing (NLP) to identify specific keywords, phrases, or documents from patient notes. Various applications of NLP were found, including the classification of symptom severity, identifying patients without documentation related to advance care planning, and monitoring online support group chat data.

Summary: This literature review indicates that AI tools can be used to support SPC clinicians in decision-making and reduce manual workload, leading to potentially improved care and outcomes for cancer patients. Emerging data from prospective studies supports the clinical benefit of these tools; however, more rigorous clinical validation is required before AI is routinely adopted in the SPC clinical workflow.

人工智能应用于癌症患者支持和姑息治疗的最新进展。
综述目的:人工智能(AI)是一种变革性技术,有可能改善和加强支持性和姑息性护理(SPC)的临床工作流程。本研究的目的是概述最近将人工智能应用于癌症患者SPC的研究。最近的发现:在2020年至2022年期间,确定了29项相关研究,并将其分为两种应用:预测建模和文本筛选。预测建模使用机器学习和/或深度学习算法来预测临床结果。大多数研究侧重于预测6个月内的短期死亡风险或生存率,而其他研究则使用模型来预测接受治疗的患者的并发症,并预测对SPC服务的需求。文本筛选通常使用自然语言处理(NLP)从患者笔记中识别特定的关键词、短语或文档。发现了NLP的各种应用,包括症状严重程度的分类、在没有与预先护理计划相关文件的情况下识别患者,以及监测在线支持群聊数据。摘要:这篇文献综述表明,人工智能工具可用于支持SPC临床医生的决策和减少人工工作量,从而潜在地改善癌症患者的护理和结果。前瞻性研究的新数据支持这些工具的临床益处;然而,在SPC临床工作流程中常规采用人工智能之前,需要更严格的临床验证。
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
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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