Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning

By Zhuoran Wang, Anoop D. Shah, A. R. Tate, S. Denaxas, John Shawe-Taylor, Harry Hemingway. 2012

110 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “Furthermore, semi-supervised methods have also been investigated for computational phenotyping [136, 137], which have the potential to significantly reduce the amount of labeling work and simultaneously retain high accuracy.”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “For example, a semisupervised approach for the extraction of diagnosis information from unstructured EHRs is presented in [38].”
Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification (2016, background): “Previous work in semi-supervised learning of the EHR relies on closed source commercial software [19], and natural language processing of free text fields to match clinical diagnosis [20,21].”