ArticlePloS one2012
Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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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
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Strategies for improving physician documentation in the emergency department: a systematic review.BMC emergency medicine · 2018Pooled it
- Enhancing substance use detection in clinical notes with large language models.Drug and alcohol dependence · 2025Article
- Extracting epilepsy-related information from unstructured clinic letters using large language models.Epilepsia · 2025Article
- Enhancing Substance Use Detection in Clinical Notes with Large Language Models.Research square · 2025Article
- Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.BMC medical informatics and decision making · 2025Article
- AI Horizons in Indian Healthcare: A Vision for Transformation and Equity.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2024Article
- Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.JMIR medical informatics · 2022Review
- Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications.IEEE journal of biomedical and health informatics · 2022Article
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.Journal of asthma and allergy · 2022Review
- Developing Clinical Prediction Models Using Primary Care Electronic Health Record Data: The Impact of Data Preparation Choices on Model Performance.Frontiers in epidemiology · 2022Article
- Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports.Journal of healthcare engineering · 2021Article
- Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper.Journal of medical Internet research · 2020Article
- Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study.JMIR medical informatics · 2020Article
- State-of-the-Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System.Journal of the American Heart Association · 2020Review
- Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care.The EPMA journal · 2019Review
- Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death.Journal of biomedical semantics · 2019Article
- Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication.Journal of biomedical informatics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundElectronic health records are invaluable for medical research, but much of the information is recorded as unstructured free text which is time-consuming to review manually.
aimTo develop an algorithm to identify relevant free texts automatically based on labelled examples.
methodsWe developed a novel machine learning algorithm, the 'Semi-supervised Set Covering Machine' (S3CM), and tested its ability to detect the presence of coronary angiogram results and ovarian cancer diagnoses in free text in the General Practice Research Database. For training the algorithm, we used texts classified as positive and negative according to their associated Read diagnostic codes, rather than by manual annotation. We evaluated the precision (positive predictive value) and recall (sensitivity) of S3CM in classifying unlabelled texts against the gold standard of manual review. We compared the performance of S3CM with the Transductive Vector Support Machine (TVSM), the original fully-supervised Set Covering Machine (SCM) and our 'Freetext Matching Algorithm' natural language processor.
resultsOnly 60% of texts with Read codes for angiogram actually contained angiogram results. However, the S3CM algorithm achieved 87% recall with 64% precision on detecting coronary angiogram results, outperforming the fully-supervised SCM (recall 78%, precision 60%) and TSVM (recall 2%, precision 3%). For ovarian cancer diagnoses, S3CM had higher recall than the other algorithms tested (86%). The Freetext Matching Algorithm had better precision than S3CM (85% versus 74%) but lower recall (62%).
conclusionsOur novel S3CM machine learning algorithm effectively detected free texts in primary care records associated with angiogram results and ovarian cancer diagnoses, after training on pre-classified test sets. It should be easy to adapt to other disease areas as it does not rely on linguistic rules, but needs further testing in other electronic health record datasets.
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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.