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Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level an...

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Autores principales: Velupillai, Sumithra, Suominen, Hanna, Liakata, Maria, Roberts, Angus, Shah, Anoop D., Morley, Katherine, Osborn, David, Hayes, Joseph, Stewart, Robert, Downs, Johnny, Chapman, Wendy, Dutta, Rina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986921/
https://www.ncbi.nlm.nih.gov/pubmed/30368002
http://dx.doi.org/10.1016/j.jbi.2018.10.005
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author Velupillai, Sumithra
Suominen, Hanna
Liakata, Maria
Roberts, Angus
Shah, Anoop D.
Morley, Katherine
Osborn, David
Hayes, Joseph
Stewart, Robert
Downs, Johnny
Chapman, Wendy
Dutta, Rina
author_facet Velupillai, Sumithra
Suominen, Hanna
Liakata, Maria
Roberts, Angus
Shah, Anoop D.
Morley, Katherine
Osborn, David
Hayes, Joseph
Stewart, Robert
Downs, Johnny
Chapman, Wendy
Dutta, Rina
author_sort Velupillai, Sumithra
collection PubMed
description The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.
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spelling pubmed-69869212020-01-28 Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances Velupillai, Sumithra Suominen, Hanna Liakata, Maria Roberts, Angus Shah, Anoop D. Morley, Katherine Osborn, David Hayes, Joseph Stewart, Robert Downs, Johnny Chapman, Wendy Dutta, Rina J Biomed Inform Article The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation. 2018-12-01 2018-10-24 /pmc/articles/PMC6986921/ /pubmed/30368002 http://dx.doi.org/10.1016/j.jbi.2018.10.005 Text en http://creativecommons.org/licenses/BY/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
spellingShingle Article
Velupillai, Sumithra
Suominen, Hanna
Liakata, Maria
Roberts, Angus
Shah, Anoop D.
Morley, Katherine
Osborn, David
Hayes, Joseph
Stewart, Robert
Downs, Johnny
Chapman, Wendy
Dutta, Rina
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
title Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
title_full Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
title_fullStr Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
title_full_unstemmed Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
title_short Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
title_sort using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6986921/
https://www.ncbi.nlm.nih.gov/pubmed/30368002
http://dx.doi.org/10.1016/j.jbi.2018.10.005
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