TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records

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 computat...

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Autores principales:	Frank Po-Yen Lin, Adrian Pokorny, Christina Teng, Richard J. Epstein
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/beee489bea1846b2bde036d07648f143
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spelling	oai:doaj.org-article:beee489bea1846b2bde036d07648f1432021-12-02T12:32:11ZTEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records10.1038/s41598-017-07111-02045-2322https://doaj.org/article/beee489bea1846b2bde036d07648f1432017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07111-0https://doaj.org/toc/2045-2322Abstract 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 Text-based Exploratory Pattern Analyser for Prognosticator and Associator 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.Frank Po-Yen LinAdrian PokornyChristina TengRichard J. EpsteinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Frank Po-Yen Lin Adrian Pokorny Christina Teng Richard J. Epstein TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
description	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 Text-based Exploratory Pattern Analyser for Prognosticator and Associator 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.
format	article
author	Frank Po-Yen Lin Adrian Pokorny Christina Teng Richard J. Epstein
author_facet	Frank Po-Yen Lin Adrian Pokorny Christina Teng Richard J. Epstein
author_sort	Frank Po-Yen Lin
title	TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
title_short	TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
title_full	TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
title_fullStr	TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
title_full_unstemmed	TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
title_sort	tepapa: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/beee489bea1846b2bde036d07648f143
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TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records

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