Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning
journal contributionposted on 2023-06-08, 12:13 authored by Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway
Background: Electronic 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. Aim: To develop an algorithm to identify relevant free texts automatically based on labelled examples. Methods: We 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. Results: Only 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%). Conclusions: Our 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.
PublisherPublic Library of Science
Department affiliated with
- Primary Care and Public Health Publications
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