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
星黛露完成签到,获得积分10
刚刚
pincoudegushi发布了新的文献求助10
1秒前
2秒前
QIYU发布了新的文献求助10
2秒前
共享精神应助烹全鱼宴采纳,获得30
2秒前
2秒前
哈哈发布了新的文献求助10
3秒前
栗爷完成签到,获得积分0
3秒前
3秒前
倒置的脚印完成签到,获得积分10
3秒前
艺心完成签到,获得积分10
3秒前
peng完成签到,获得积分10
3秒前
3秒前
4秒前
Ava应助asww采纳,获得10
4秒前
雨田完成签到,获得积分0
4秒前
4秒前
lfg完成签到,获得积分10
5秒前
活泼的便当完成签到,获得积分10
5秒前
流浪完成签到,获得积分10
5秒前
6秒前
6秒前
lili发布了新的文献求助10
7秒前
luchong完成签到,获得积分10
7秒前
LDDD发布了新的文献求助10
8秒前
onethree完成签到 ,获得积分10
9秒前
完美世界应助LZY采纳,获得10
9秒前
雪白起眸发布了新的文献求助10
9秒前
9秒前
扶手发布了新的文献求助10
9秒前
9秒前
genomed应助pray采纳,获得10
9秒前
江河发布了新的文献求助10
10秒前
不配.应助joyboy采纳,获得10
10秒前
gl发布了新的文献求助10
10秒前
10秒前
10秒前
梨子完成签到,获得积分10
12秒前
李昆朋完成签到,获得积分10
12秒前
lili完成签到,获得积分10
12秒前
高分求助中
Write Like a Chemist: A Guide and Resource (第二版) 600
Mixed-anion Compounds 600
A new species of Velataspis (Hemiptera Coccoidea Diaspididae) from tea in Assam 500
Earth System Geophysics 500
Aspects of Babylonian celestial divination: the lunar eclipse tablets of Enūma Anu Enlil 500
Geochemistry, 2nd Edition 地球化学经典教科书第二版 401
2024 Medicinal Chemistry Reviews 400
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 3201273
求助须知:如何正确求助?哪些是违规求助? 2850854
关于积分的说明 8074942
捐赠科研通 2514733
什么是DOI,文献DOI怎么找? 1347411
科研通“疑难数据库(出版商)”最低求助积分说明 640427
邀请新用户注册赠送积分活动 610621