Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.

Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.

<h4>Background</h4>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.<h4>Aim</h4>To develop an algorithm to identify relevant free texts automatically bas...

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Autores principales: Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2012
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Medicine
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Science
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Acceso en línea:https://doaj.org/article/d17e9e6b6da44b479e895c355a40dd42
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spelling oai:doaj.org-article:d17e9e6b6da44b479e895c355a40dd422021-11-18T07:29:48ZExtracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.1932-620310.1371/journal.pone.0030412https://doaj.org/article/d17e9e6b6da44b479e895c355a40dd422012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22276193/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Aim</h4>To develop an algorithm to identify relevant free texts automatically based on labelled examples.<h4>Methods</h4>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.<h4>Results</h4>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%).<h4>Conclusions</h4>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.Zhuoran WangAnoop D ShahA Rosemary TateSpiros DenaxasJohn Shawe-TaylorHarry HemingwayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 1, p e30412 (2012)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Zhuoran Wang
Anoop D Shah
A Rosemary Tate
Spiros Denaxas
John Shawe-Taylor
Harry Hemingway
Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
description <h4>Background</h4>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.<h4>Aim</h4>To develop an algorithm to identify relevant free texts automatically based on labelled examples.<h4>Methods</h4>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.<h4>Results</h4>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%).<h4>Conclusions</h4>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.
format article
author Zhuoran Wang
Anoop D Shah
A Rosemary Tate
Spiros Denaxas
John Shawe-Taylor
Harry Hemingway
author_facet Zhuoran Wang
Anoop D Shah
A Rosemary Tate
Spiros Denaxas
John Shawe-Taylor
Harry Hemingway
author_sort Zhuoran Wang
title Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
title_short Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
title_full Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
title_fullStr Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
title_full_unstemmed Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
title_sort extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
publisher Public Library of Science (PLoS)
publishDate 2012
url https://doaj.org/article/d17e9e6b6da44b479e895c355a40dd42
work_keys_str_mv AT zhuoranwang extractingdiagnosesandinvestigationresultsfromunstructuredtextinelectronichealthrecordsbysemisupervisedmachinelearning
AT anoopdshah extractingdiagnosesandinvestigationresultsfromunstructuredtextinelectronichealthrecordsbysemisupervisedmachinelearning
AT arosemarytate extractingdiagnosesandinvestigationresultsfromunstructuredtextinelectronichealthrecordsbysemisupervisedmachinelearning
AT spirosdenaxas extractingdiagnosesandinvestigationresultsfromunstructuredtextinelectronichealthrecordsbysemisupervisedmachinelearning
AT johnshawetaylor extractingdiagnosesandinvestigationresultsfromunstructuredtextinelectronichealthrecordsbysemisupervisedmachinelearning
AT harryhemingway extractingdiagnosesandinvestigationresultsfromunstructuredtextinelectronichealthrecordsbysemisupervisedmachinelearning
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