机器学习管道导航:住院病人谵妄预测模型范围综述。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2023-07-01 DOI:10.1136/bmjhci-2023-100767
Tom Strating, Leila Shafiee Hanjani, Ida Tornvall, Ruth Hubbard, Ian A Scott
{"title":"机器学习管道导航:住院病人谵妄预测模型范围综述。","authors":"Tom Strating, Leila Shafiee Hanjani, Ida Tornvall, Ruth Hubbard, Ian A Scott","doi":"10.1136/bmjhci-2023-100767","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Early identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.</p><p><strong>Methods: </strong>We searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.</p><p><strong>Results: </strong>Among 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.</p><p><strong>Discussion: </strong>ML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.</p><p><strong>Conclusion: </strong>This review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ec/23/bmjhci-2023-100767.PMC10335592.pdf","citationCount":"0","resultStr":"{\"title\":\"Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models.\",\"authors\":\"Tom Strating, Leila Shafiee Hanjani, Ida Tornvall, Ruth Hubbard, Ian A Scott\",\"doi\":\"10.1136/bmjhci-2023-100767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Early identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.</p><p><strong>Methods: </strong>We searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.</p><p><strong>Results: </strong>Among 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.</p><p><strong>Discussion: </strong>ML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.</p><p><strong>Conclusion: </strong>This review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.</p>\",\"PeriodicalId\":9050,\"journal\":{\"name\":\"BMJ Health & Care Informatics\",\"volume\":\"30 1\",\"pages\":\"\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ec/23/bmjhci-2023-100767.PMC10335592.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ Health & Care Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjhci-2023-100767\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Health & Care Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjhci-2023-100767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

摘要

目的:早期识别有谵妄风险的住院病人并采取预防措施可避免高达 40% 的谵妄病例。基于机器学习(ML)的预测模型可实现风险分层和有针对性的干预,但要确定其目前的发展状况,需要对近期文献进行范围界定:我们检索了截至 2022 年 6 月的十个数据库,以了解基于 ML 的谵妄预测模型的研究情况。合格标准包括:在成人住院患者中至少使用了一种ML预测方法;以英语发表;至少报告了一项性能指标(接收者-操作者曲线下面积(AUROC)、灵敏度、特异性、阳性或阴性预测值)。对纳入的模型按其成熟阶段进行分类,并对临床实践中的性能、实用性和用户接受度进行评估:结果:在筛选出的 921 项研究中,有 39 项符合资格标准。研究结果表明:在筛选出的 921 项研究中,有 39 项研究符合资格标准,其中的硅学性能一直很高(中位数 AUROC:0.85);然而,只有 6 篇文章(15.4%)报告了外部验证,显示出性能下降(中位数 AUROC:0.75)。三项关于在临床工作流程中部署模型的研究(7.7%)报告了较高的准确性(中位数AUROC:0.92)和较高的用户接受度:讨论:ML 模型具有在症状出现前识别有谵妄风险的住院患者的潜力。然而,经过外部验证的模型很少,在临床环境中进行前瞻性评估的模型更少:本综述证实了在医院环境中使用 ML 预测谵妄风险的研究正在迅速发展。我们的研究结果为开发人员和临床医生提供了有关当前 ML 谵妄预测应用的优势和局限性的见解,这些应用旨在支持而非取代临床医生的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models.

Objectives: Early identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.

Methods: We searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.

Results: Among 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.

Discussion: ML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.

Conclusion: This review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
期刊最新文献
Scaling equitable artificial intelligence in healthcare with machine learning operations. Understanding prescribing errors for system optimisation: the technology-related error mechanism classification. Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan. PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis. Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1