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
刚刚
刚刚
小xy发布了新的文献求助10
1秒前
汉堡包应助Summer采纳,获得30
2秒前
miss1995发布了新的文献求助10
2秒前
3秒前
mll发布了新的文献求助10
4秒前
4秒前
雪白音响发布了新的文献求助10
5秒前
赘婿应助AFsumo采纳,获得10
5秒前
Zephyr发布了新的文献求助10
5秒前
和路雪发布了新的文献求助10
6秒前
37发布了新的文献求助10
6秒前
6秒前
6秒前
6秒前
7秒前
7秒前
tfsn20发布了新的文献求助10
8秒前
大林发布了新的文献求助10
9秒前
10秒前
511发布了新的文献求助10
10秒前
Owen应助科研人员采纳,获得10
12秒前
CipherSage应助知性的耳机采纳,获得10
12秒前
领导范儿应助wang采纳,获得10
12秒前
七分糖发布了新的文献求助10
12秒前
13秒前
金刚经应助37采纳,获得10
14秒前
14秒前
小绵羊的酸奶盖完成签到,获得积分10
14秒前
mll完成签到,获得积分10
14秒前
收敛完成签到,获得积分10
16秒前
JamesPei应助吉吉采纳,获得10
16秒前
AFsumo发布了新的文献求助10
16秒前
17秒前
18秒前
一页发布了新的文献求助10
18秒前
19秒前
万能图书馆应助guyu采纳,获得30
19秒前
梦闲人完成签到,获得积分10
19秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
Aspects of Babylonian celestial divination: the lunar eclipse tablets of Enūma Anu Enlil 500
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
India's foreign trade policy and its performance in the world economy 450
Structural Inorganic Chemistry 400
Dictionary of socialism 350
Mixed-anion Compounds 300
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 3196121
求助须知:如何正确求助?哪些是违规求助? 2844892
关于积分的说明 8052117
捐赠科研通 2509514
什么是DOI,文献DOI怎么找? 1341768
科研通“疑难数据库(出版商)”最低求助积分说明 639262
邀请新用户注册赠送积分活动 608445