دورية أكاديمية
Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic
| العنوان: | Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic |
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| المؤلفون: | Neuraz, Antoine, Lerner, Ivan, Digan, William, Paris, Nicolas, Tsopra, Rosy, Rogier, Alice, Baudoin, David, Cohen, Kevin Bretonnel, Burgun, Anita, Garcelon, Nicolas, Rance, Bastien |
| المساهمون: | Hôpital Necker - Enfants Malades AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Hôpital Européen Georges Pompidou APHP (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Information, Langue Ecrite et Signée (ILES), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Colorado Denver, Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), AP-HP/Universities/INSERM COVID-19 Research Collaboration, AP-HP COVID CDR Initiative: Pierre-Yves Ancel, Alain Bauchet, Nathanaël Beeker, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Breant, Anita Burgun, Fabrice Carrat, Charlotte Caucheteux, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Duclos, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Anne Sophie Jannot, Jerome Lambert, Richard Layese, Judith Leblanc, Léo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurélien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Bastien Rance, Hélène Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien van Gysel, Gaël Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal |
| المصدر: | ISSN: 1438-8871 ; Journal of Medical Internet Research ; https://hal.science/hal-03738905Test ; Journal of Medical Internet Research, 2020, 22 (8), pp.e20773. ⟨10.2196/20773⟩. |
| بيانات النشر: | HAL CCSD JMIR Publications |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | response, COVID-19, electronic health records, emergent disease, informatics, medication information, natural language processing, public health, MESH: Betacoronavirus, MESH: COVID-19, MESH: Time Factors, MESH: Calcium Channel Blockers, MESH: Coronavirus Infections, MESH: Data Mining, MESH: Electronic Health Records, MESH: Humans, MESH: Hypertension, MESH: Natural Language Processing, MESH: Pandemics, MESH: Pneumonia, Viral, MESH: SARS-CoV-2, [INFO]Computer Science [cs], [SDV]Life Sciences [q-bio] |
| الوصف: | International audience ; Background A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. Objective The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). Methods We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. Results In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/32759101; hal-03738905; https://hal.science/hal-03738905Test; PUBMED: 32759101; PUBMEDCENTRAL: PMC7431235 |
| DOI: | 10.2196/20773 |
| الإتاحة: | https://doi.org/10.2196/20773Test https://hal.science/hal-03738905Test |
| رقم الانضمام: | edsbas.60DD17F9 |
| قاعدة البيانات: | BASE |
| DOI: | 10.2196/20773 |
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