已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

时间轴 计算机科学 变压器 健康信息学 情报检索 病历 人工智能 自然语言处理 数据科学 医学 公共卫生 工程类 护理部 考古 放射科 电压 电气工程 历史
作者
Željko Kraljević,Daniel Bean,Anthony Shek,Rebecca Bendayan,Harry Hemingway,Joshua Au Yeung,Alexander Deng,Alfred Balston,Jack Ross,Esther Idowu,James Teo,Richard Dobson
出处
期刊:The Lancet Digital Health [Elsevier]
卷期号:6 (4): e281-e290 被引量:14
标识
DOI:10.1016/s2589-7500(24)00025-6
摘要

BackgroundAn electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments).MethodsForesight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall.FindingsForesight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91–100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required.InterpretationForesight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes.FundingNational Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
嗯哼应助sch采纳,获得20
1秒前
5秒前
Diligency完成签到 ,获得积分10
10秒前
amengptsd完成签到,获得积分10
12秒前
科研狗的春天完成签到 ,获得积分10
15秒前
17秒前
王sir完成签到 ,获得积分10
18秒前
18秒前
辜月十二完成签到 ,获得积分10
18秒前
铮铮完成签到,获得积分10
18秒前
19秒前
susan完成签到 ,获得积分10
19秒前
忧郁荔枝完成签到 ,获得积分10
20秒前
紫薯球完成签到,获得积分10
20秒前
十一发布了新的文献求助10
21秒前
xx完成签到 ,获得积分10
22秒前
24秒前
26秒前
星叶完成签到 ,获得积分10
26秒前
27秒前
打打应助Chenzr采纳,获得10
28秒前
GRH发布了新的文献求助10
29秒前
钥匙发布了新的文献求助10
30秒前
bean完成签到 ,获得积分10
30秒前
31秒前
十一完成签到,获得积分10
32秒前
王逗逗发布了新的文献求助10
32秒前
WerWu完成签到,获得积分10
32秒前
cs完成签到 ,获得积分10
33秒前
脱壳金蝉完成签到,获得积分10
33秒前
wyx完成签到,获得积分10
34秒前
四月的海棠完成签到 ,获得积分10
34秒前
nanfang完成签到 ,获得积分10
36秒前
落落完成签到 ,获得积分0
37秒前
田様应助活力的采枫采纳,获得10
38秒前
shierfang完成签到 ,获得积分10
38秒前
宋鹏炜完成签到,获得积分20
38秒前
思源应助樱桃味的火苗采纳,获得10
39秒前
wang0626完成签到 ,获得积分10
39秒前
youngyang完成签到 ,获得积分10
39秒前
高分求助中
Evolution 3rd edition 1500
保险藏宝图 1000
Lire en communiste 1000
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 700
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 700
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3183597
求助须知:如何正确求助?哪些是违规求助? 2833535
关于积分的说明 7994874
捐赠科研通 2495851
什么是DOI,文献DOI怎么找? 1331689
科研通“疑难数据库(出版商)”最低求助积分说明 636409
邀请新用户注册赠送积分活动 603581