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

BACKGROUND: An 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). METHODS: Foresight 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. FINDINGS: Foresight 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. INTERPRETATION: Foresight 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. FUNDING: National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.

Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records.

As foundation doctors, we have often found ourselves informing patients that a certain aspect of their medical information cannot be immediately found, either because it is on an electronic system we cannot access, or it is in a hospital that is unlinked to our own. Unsurprisingly, this frequently leaves patients flabbergasted and confused. We started to wonder: if patients' data are entered onto an electronic system: where do those data go? If medical data are searched for, where do those data come from? Why are there so many hidden sources of information that clinicians cannot access? In an ever-increasing digital sphere, electronic data will be the future of holistic health and social care planning, impacting every clinician's day-to-day role. From electronic healthcare records to the use of artificial intelligence solutions, this article will serve as an introduction to how data flows in modern healthcare systems.

Dietary nitrate prevents progression of carotid subclinical atherosclerosis through blood pressure-independent mechanisms in patients with or at risk of type 2 diabetes mellitus.

AIMS: To test if 6 months' intervention with dietary nitrate and spironolactone could affect carotid subclinical atherosclerosis and stiffness, respectively, vs. placebo/doxazosin, to control for blood pressure (BP). METHODS: A subgroup of participants in our double-blind, randomized-controlled, factorial VaSera trial had carotid imaging. Patients with hypertension and with/at risk of type 2 diabetes were randomized to active nitrate-containing beetroot juice or placebo nitrate-depleted juice, and spironolactone or doxazosin. Vascular ultrasound for carotid diameter (CD, mm) and intima-media thickness (CIMT, mm) was performed at baseline, 3- and 6-months. Carotid local stiffness (CS, m/s) was estimated from aortic pulse pressure (Arteriograph) and carotid lumen area. Data were analysed by modified intention to treat and using mixed-model effect, adjusted for confounders. RESULTS: In total, 93 subjects had a baseline evaluation and 86% had follow-up data. No statistical interactions occurred between the juice and drug arms and BP was similar between the juices and between the drugs. Nitrate-containing vs. placebo juice significantly lowered CIMT (-0.06 [95% confidence interval -0.12, -0.01], P = .034), an overall difference of ~8% relative to baseline; but had no effect on CD or CS. Doxazosin appeared to reduce CS from baseline (-0.34 [-0.62, -0.06]) however, no difference was detected vs. spironolactone (-0.15 [-0.46, 0.16]). No differences were detected between spironolactone or doxazosin on CIMT and CD. CONCLUSIONS: Our results show that 6 months' intervention with dietary nitrate influences vascular remodelling, but not carotid stiffness or diameter. Neither spironolactone nor doxazosin had a BP-independent effect on carotid structure and function.