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
香冢弃了残红完成签到,获得积分10
刚刚
小乔应助远方采纳,获得10
1秒前
林齐完成签到 ,获得积分10
2秒前
李爱国应助nancyzhao采纳,获得10
2秒前
jiangxuexue完成签到,获得积分10
2秒前
mahuahua完成签到,获得积分10
2秒前
Jason完成签到,获得积分10
2秒前
阳光的安南完成签到,获得积分10
2秒前
joe完成签到,获得积分10
2秒前
畅跑daily完成签到,获得积分10
3秒前
3秒前
ShujunOvO发布了新的文献求助10
3秒前
小二郎应助apple810采纳,获得10
3秒前
Leo完成签到,获得积分10
5秒前
Xixia完成签到,获得积分10
6秒前
谢必安完成签到 ,获得积分10
6秒前
saberynn发布了新的文献求助10
6秒前
7秒前
昏睡的眼神完成签到 ,获得积分10
8秒前
zz完成签到,获得积分10
8秒前
铁甲小宝完成签到,获得积分10
8秒前
一一完成签到,获得积分10
8秒前
胡英俊完成签到,获得积分10
9秒前
9秒前
Tammy完成签到,获得积分10
9秒前
9秒前
烟花应助敬老院N号采纳,获得10
9秒前
李健应助敬老院N号采纳,获得10
9秒前
msl2023完成签到,获得积分10
11秒前
坦率的匪发布了新的文献求助20
11秒前
11秒前
Ya完成签到 ,获得积分10
12秒前
草哥发布了新的文献求助10
12秒前
Riverside完成签到,获得积分10
12秒前
是赤赤呀完成签到,获得积分10
12秒前
arielice完成签到,获得积分10
13秒前
凌露完成签到 ,获得积分0
13秒前
ShujunOvO完成签到,获得积分10
13秒前
Yeah完成签到,获得积分10
14秒前
刘刘发布了新的文献求助20
14秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
ALA生合成不全マウスでの糖代謝異常の分子機構解析 520
安全防范技术与工程 500
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
2024 Medicinal Chemistry Reviews 400
Актуализированная стратиграфическая схема триасовых отложений Прикаспийского региона. Объяснительная записка 360
Dictionary of socialism 350
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 3192935
求助须知:如何正确求助?哪些是违规求助? 2841978
关于积分的说明 8036528
捐赠科研通 2505759
什么是DOI,文献DOI怎么找? 1338749
科研通“疑难数据库(出版商)”最低求助积分说明 638486
邀请新用户注册赠送积分活动 606990