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Next generation phenotyping using narrative reports in a rare disease clinical data warehouse
BACKGROUND: Secondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children’s Hospital contains data collected during normal care for thousands of p...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984368/ https://www.ncbi.nlm.nih.gov/pubmed/29855327 http://dx.doi.org/10.1186/s13023-018-0830-6 |
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author | Garcelon, Nicolas Neuraz, Antoine Salomon, Rémi Bahi-Buisson, Nadia Amiel, Jeanne Picard, Capucine Mahlaoui, Nizar Benoit, Vincent Burgun, Anita Rance, Bastien |
author_facet | Garcelon, Nicolas Neuraz, Antoine Salomon, Rémi Bahi-Buisson, Nadia Amiel, Jeanne Picard, Capucine Mahlaoui, Nizar Benoit, Vincent Burgun, Anita Rance, Bastien |
author_sort | Garcelon, Nicolas |
collection | PubMed |
description | BACKGROUND: Secondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children’s Hospital contains data collected during normal care for thousands of patients. Dr. Warehouse is oriented toward the exploration of clinical narratives. In this study, we present our method to find phenotypes associated with diseases of interest. METHODS: We leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. We applied our method in six use cases: phenotypes associated with the Rett, Lowe, Silver Russell, Bardet-Biedl syndromes, DOCK8 deficiency and Activated PI3-kinase Delta Syndrome (APDS). We asked domain experts to evaluate the relevance of the top-50 (for frequency and TF-IDF) phenotypes identified by Dr. Warehouse and computed the average precision and mean average precision. RESULTS: Experts concluded that between 16 and 39 phenotypes could be considered as relevant in the top-50 phenotypes ranked by descending frequency discovered by Dr. Warehouse (resp. between 11 and 41 for TF-IDF). Average precision ranges from 0.55 to 0.91 for frequency and 0.52 to 0.95 for TF-IDF. Mean average precision was 0.79. Our study suggests that phenotypes identified in clinical narratives stored in Electronic Health Record can provide rare disease specialists with candidate phenotypes that can be used in addition to the literature. CONCLUSIONS: Clinical Data Warehouses can be used to perform Next Generation Phenotyping, especially in the context of rare diseases. We have developed a method to detect phenotypes associated with a group of patients using medical concepts extracted from free-text clinical narratives. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13023-018-0830-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5984368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59843682018-06-07 Next generation phenotyping using narrative reports in a rare disease clinical data warehouse Garcelon, Nicolas Neuraz, Antoine Salomon, Rémi Bahi-Buisson, Nadia Amiel, Jeanne Picard, Capucine Mahlaoui, Nizar Benoit, Vincent Burgun, Anita Rance, Bastien Orphanet J Rare Dis Research BACKGROUND: Secondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children’s Hospital contains data collected during normal care for thousands of patients. Dr. Warehouse is oriented toward the exploration of clinical narratives. In this study, we present our method to find phenotypes associated with diseases of interest. METHODS: We leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. We applied our method in six use cases: phenotypes associated with the Rett, Lowe, Silver Russell, Bardet-Biedl syndromes, DOCK8 deficiency and Activated PI3-kinase Delta Syndrome (APDS). We asked domain experts to evaluate the relevance of the top-50 (for frequency and TF-IDF) phenotypes identified by Dr. Warehouse and computed the average precision and mean average precision. RESULTS: Experts concluded that between 16 and 39 phenotypes could be considered as relevant in the top-50 phenotypes ranked by descending frequency discovered by Dr. Warehouse (resp. between 11 and 41 for TF-IDF). Average precision ranges from 0.55 to 0.91 for frequency and 0.52 to 0.95 for TF-IDF. Mean average precision was 0.79. Our study suggests that phenotypes identified in clinical narratives stored in Electronic Health Record can provide rare disease specialists with candidate phenotypes that can be used in addition to the literature. CONCLUSIONS: Clinical Data Warehouses can be used to perform Next Generation Phenotyping, especially in the context of rare diseases. We have developed a method to detect phenotypes associated with a group of patients using medical concepts extracted from free-text clinical narratives. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13023-018-0830-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-31 /pmc/articles/PMC5984368/ /pubmed/29855327 http://dx.doi.org/10.1186/s13023-018-0830-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Garcelon, Nicolas Neuraz, Antoine Salomon, Rémi Bahi-Buisson, Nadia Amiel, Jeanne Picard, Capucine Mahlaoui, Nizar Benoit, Vincent Burgun, Anita Rance, Bastien Next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
title | Next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
title_full | Next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
title_fullStr | Next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
title_full_unstemmed | Next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
title_short | Next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
title_sort | next generation phenotyping using narrative reports in a rare disease clinical data warehouse |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984368/ https://www.ncbi.nlm.nih.gov/pubmed/29855327 http://dx.doi.org/10.1186/s13023-018-0830-6 |
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