ArticlePLOS digital health2023
Hospital-wide natural language processing summarising the health data of 1 million patients.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Artificial intelligence methods to detect heart failure with preserved ejection fraction within electronic health records: an equitable disease detection model.European heart journal. Digital health · 2026Article
- Exploring the role of artificial intelligence in orthopedic medical education: A narrative review.Journal of clinical orthopaedics and trauma · 2025Article
- Artificial Intelligence in Rheumatology: Quo Vadis?Mediterranean journal of rheumatology · 2025Article
- Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.BMC medical informatics and decision making · 2024Article
- Natural language processing data services for healthcare providers.BMC medical informatics and decision making · 2024Review
- Development of a natural language processing pipeline for assessment of cardiovascular risk in myeloproliferative neoplasms.HemaSphere · 2024Article
- Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study.The Lancet. Digital health · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.