TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
Identifieur interne : 000051 ( Main/Exploration ); précédent : 000050; suivant : 000052TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
Auteurs : Frank Po-Yen Lin ; Adrian Pokorny ; Christina Teng ; Richard J. EpsteinSource :
- Scientific Reports [ 2045-2322 ] ; 2017.
Abstract
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed
Url:
DOI: 10.1038/s41598-017-07111-0
PubMed: 28761061
PubMed Central: 5537364
Affiliations:
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Epstein</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9119 2677</institution-id><institution-id institution-id-type="GRID">grid.437825.f</institution-id><institution>Department of Oncology,</institution><institution>St Vincent’s Hospital & The Kinghorn Cancer Centre,</institution></institution-wrap>Darlinghurst, NSW Australia</nlm:aff><wicri:noCountry code="subfield">NSW Australia</wicri:noCountry></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9983 6924</institution-id><institution-id institution-id-type="GRID">grid.415306.5</institution-id><institution>Garvan Institute of Medical Research,</institution></institution-wrap>Darlinghurst, NSW Australia</nlm:aff><wicri:noCountry code="subfield">NSW Australia</wicri:noCountry></affiliation></author></analytic><series><title level="j">Scientific Reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed <italic>T</italic>ext-based <italic>E</italic>xploratory <italic>P</italic>attern <italic>A</italic>nalyser for <italic>P</italic>rognosticator and <italic>A</italic>ssociator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest. When we applied TEPAPA to a cohort of head and neck squamous cell carcinoma patients, plausible concepts known to be correlated with human papilloma virus (HPV) status were identified from the EMR text, including site of primary disease, tumour stage, pathologic characteristics, and treatment modalities. Similarly, correlates of other variables (including gender, nodal status, recurrent disease, smoking and alcohol status) were also reliably recovered. Using highly-associated patterns as covariates, a patient’s HPV status was classifiable using a bootstrap analysis with a mean area under the ROC curve of 0.861, suggesting its predictive utility in supporting EMR-based phenotyping tasks. These data support using this integrative approach to efficiently identify disease-associated factors from unstructured EMR narratives, and thus to efficiently generate testable hypotheses.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Frankovich, J" uniqKey="Frankovich J">J Frankovich</name></author><author><name sortKey="Longhurst, Ca" uniqKey="Longhurst C">CA Longhurst</name></author><author><name sortKey="Sutherland, Sm" uniqKey="Sutherland S">SM Sutherland</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zheng, K" uniqKey="Zheng K">K Zheng</name></author><author><name sortKey="Mei, Q" uniqKey="Mei Q">Q Mei</name></author><author><name sortKey="Hanauer, Da" uniqKey="Hanauer D">DA Hanauer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kahn, Mg" uniqKey="Kahn M">MG Kahn</name></author><author><name sortKey="Weng, C" uniqKey="Weng C">C 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Bland</name></author><author><name sortKey="Altman, Dg" uniqKey="Altman D">DG Altman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Benjamini, Y" uniqKey="Benjamini Y">Y Benjamini</name></author><author><name sortKey="Hochberg, Y" uniqKey="Hochberg Y">Y Hochberg</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Hripcsak, G" uniqKey="Hripcsak G">G Hripcsak</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hripcsak, G" uniqKey="Hripcsak G">G Hripcsak</name></author><author><name sortKey="Albers, Dj" uniqKey="Albers D">DJ Albers</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hersh, Wr" uniqKey="Hersh W">WR Hersh</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list></list><tree><noCountry><name sortKey="Epstein, Richard J" sort="Epstein, Richard J" uniqKey="Epstein R" first="Richard J." last="Epstein">Richard J. Epstein</name><name sortKey="Lin, Frank Po Yen" sort="Lin, Frank Po Yen" uniqKey="Lin F" first="Frank Po-Yen" last="Lin">Frank Po-Yen Lin</name><name sortKey="Pokorny, Adrian" sort="Pokorny, Adrian" uniqKey="Pokorny A" first="Adrian" last="Pokorny">Adrian Pokorny</name><name sortKey="Teng, Christina" sort="Teng, Christina" uniqKey="Teng C" first="Christina" last="Teng">Christina Teng</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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