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
领导范儿应助Seeone采纳,获得10
刚刚
赘婿应助缪连虎采纳,获得10
1秒前
xhl发布了新的文献求助10
1秒前
严美娜完成签到,获得积分10
1秒前
程程程程完成签到,获得积分10
2秒前
xiuxiu发布了新的文献求助10
3秒前
空山新雨发布了新的文献求助10
3秒前
爆米花应助sia采纳,获得10
3秒前
Lc完成签到,获得积分10
3秒前
4秒前
不配.应助Fancy采纳,获得20
5秒前
清秀涵易完成签到,获得积分20
5秒前
6秒前
6秒前
6秒前
魏文超完成签到,获得积分10
6秒前
LZY应助feifei采纳,获得10
6秒前
凶狠的盼柳完成签到,获得积分10
7秒前
叶菲菲发布了新的文献求助10
7秒前
8秒前
星月夜应助沉默的婴采纳,获得20
8秒前
8秒前
yyy发布了新的文献求助10
8秒前
Hello应助1233采纳,获得10
8秒前
Baibai发布了新的文献求助10
9秒前
10秒前
YY关注了科研通微信公众号
10秒前
线条完成签到 ,获得积分10
10秒前
tuntunliu发布了新的文献求助20
11秒前
WJ发布了新的文献求助10
11秒前
啊呜发布了新的文献求助10
12秒前
13秒前
yuanyuan完成签到,获得积分10
13秒前
Seeone完成签到,获得积分10
13秒前
14秒前
14秒前
可燃冰发布了新的文献求助10
15秒前
15秒前
chris完成签到,获得积分10
15秒前
fddsfs发布了新的文献求助10
15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
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
2-Acetyl-1-pyrroline: an important aroma component of cooked rice 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 3181539
求助须知:如何正确求助?哪些是违规求助? 2831784
关于积分的说明 7986720
捐赠科研通 2493805
什么是DOI,文献DOI怎么找? 1330348
科研通“疑难数据库(出版商)”最低求助积分说明 635955
版权声明 602955