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

{"title"=>"Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning", "type"=>"generic", "authors"=>[{"first_name"=>"Zhuoran", "last_name"=>"Wang", "scopus_author_id"=>"54901667200"}, {"first_name"=>"Anoop D.", "last_name"=>"Shah", "scopus_author_id"=>"36669369400"}, {"first_name"=>"A. Rosemary", "last_name"=>"Tate", "scopus_author_id"=>"7006891204"}, {"first_name"=>"Spiros", "last_name"=>"Denaxas", "scopus_author_id"=>"54902667300"}, {"first_name"=>"John", "last_name"=>"Shawe-Taylor", "scopus_author_id"=>"7003290763"}, {"first_name"=>"Harry", "last_name"=>"Hemingway", "scopus_author_id"=>"7004642497"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84856021032", "doi"=>"10.1371/journal.pone.0030412", "pui"=>"364114004", "pmid"=>"22276193", "scopus"=>"2-s2.0-84856021032", "issn"=>"19326203", "isbn"=>"1932-6203"}, "id"=>"9ccb7a4f-dc90-3797-af79-3d4c348b5a68", "abstract"=>"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.", "link"=>"http://www.mendeley.com/research/extracting-diagnoses-investigation-results-unstructured-text-electronic-health-records-semisupervise", "reader_count"=>96, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>2, "Researcher"=>36, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>3, "Other"=>3, "Student > Master"=>8, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>2, "Researcher"=>36, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>3, "Other"=>3, "Student > Master"=>8, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>4, "Medicine and Dentistry"=>40, "Agricultural and Biological Sciences"=>5, "Physics and Astronomy"=>1, "Psychology"=>2, "Social Sciences"=>3, "Computer Science"=>28, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>40}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>5}, "Computer Science"=>{"Computer Science"=>28}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>7}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>5, "United Kingdom"=>7, "Australia"=>1}, "group_count"=>16}

Scopus | Further Information

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Figshare

  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/351642/Figure_S1.pdf", "https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/351695/Text_S1.pdf"], "description"=>"<div><h3>Background</h3><p>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.</p> <h3>Aim</h3><p>To develop an algorithm to identify relevant free texts automatically based on labelled examples.</p> <h3>Methods</h3><p>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.</p> <h3>Results</h3><p>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%).</p> <h3>Conclusions</h3><p>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.</p> </div>", "links"=>[], "tags"=>["extracting", "diagnoses", "results", "unstructured", "records", "semi-supervised"], "article_id"=>129505, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/Extracting_Diagnoses_and_Investigation_Results_from_Unstructured_Text_in_Electronic_Health_Records_by_Semi_Supervised_Machine_Learning/129505", "title"=>"Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-01-19 02:38:25"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/690461/Figure_1.tif"], "description"=>"<p>Flow diagram showing logic of the algorithm, and definitions of positive, negative and unlabelled training sets for detection of coronary angiogram results.</p>", "links"=>[], "tags"=>["covering", "detecting", "coronary", "angiogram"], "article_id"=>360939, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Semi_Supervised_Set_Covering_Machine_for_detecting_coronary_angiogram_results_/360939", "title"=>"Semi-Supervised Set Covering Machine for detecting coronary angiogram results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-01-19 00:15:39"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/690559/Figure_2.tif"], "description"=>"<p>Flow diagram showing logic of the algorithm, and definitions of positive, negative and unlabelled training sets for detection of ovarian cancer diagnoses.</p>", "links"=>[], "tags"=>["covering", "detecting", "ovarian", "cancer"], "article_id"=>361037, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Semi_Supervised_Set_Covering_Machine_for_detecting_ovarian_cancer_diagnoses_/361037", "title"=>"Semi-Supervised Set Covering Machine for detecting ovarian cancer diagnoses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-01-19 00:17:17"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/690660/Figure_3.tif"], "description"=>"<p>List of word stem combinations selected as classification rules by for (A) coronary angiogram and (B) ovarian cancer test sets. The bars show the frequency of each rule among the combined positive, negative and unlabelled training sets. Words were stemmed in order to aid the grouping of similar words; for example ‘ovarian’, ‘ovary’ and ‘ovaries’ were all converted to the common stem ‘ovari’.</p>", "links"=>[], "tags"=>["combinations", "extracted"], "article_id"=>361138, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Word_stem_combinations_extracted_from_free_text_records_/361138", "title"=>"Word stem combinations extracted from free text records.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-01-19 00:18:58"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/690728/Table_2.xls"], "description"=>"<p>Precision (positive predictive value) is the percentage of texts positively classified by the algorithm that are true positive, and recall (sensitivity) is the percentage of all positive texts correctly classified as positive by the algorithm. F score is the harmonic mean of precision and recall. Figures in parentheses are 95% confidence intervals.</p>", "links"=>[], "tags"=>["classification", "unlabelled"], "article_id"=>361209, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Results_of_testing_classification_of_unlabelled_texts_/361209", "title"=>"Results of testing: classification of unlabelled texts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-01-19 00:20:09"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/690769/Table_3.xls"], "description"=>"<p>Mean two standard deviations from 10 experiments testing on classification at the patient level by splitting patients randomly into a training set and a test set.</p>", "links"=>[], "tags"=>["detection", "angiogram", "ovarian", "cancer"], "article_id"=>361250, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Results_of_testing_detection_rate_by_patient_of_presence_of_angiogram_results_or_ovarian_cancer_diagnosis_in_the_free_text_/361250", "title"=>"Results of testing: detection rate by patient of presence of angiogram results or ovarian cancer diagnosis in the free text.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-01-19 00:20:50"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/690805/Table_1.xls"], "description"=>"<p>Selection of free text entries for training the algorithm.</p>", "links"=>[], "tags"=>["entries"], "article_id"=>361285, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Zhuoran Wang", "Anoop D. Shah", "A. Rosemary Tate", "Spiros Denaxas", "John Shawe-Taylor", "Harry Hemingway"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0030412.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Selection_of_free_text_entries_for_training_the_algorithm_/361285", "title"=>"Selection of free text entries for training the algorithm.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-01-19 00:21:25"}

PMC Usage Stats | Further Information

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Relative Metric

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