Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England..ORCID iD: 0000-0002-4178-2980
Australian Natl Univ, CSIRO Data61, Univ Canberra, Coll Engn & Comp Sci, Canberra, ACT, Australia.;Univ Turku, Turku, Finland..
Univ Warwick, Alan Turing Inst, Dept Comp Sci, Coventry, W Midlands, England..
Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England..ORCID iD: 0000-0002-4570-9801
Show others and affiliations
2018 (English)In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 88, p. 11-19Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
ACADEMIC PRESS INC ELSEVIER SCIENCE , 2018. Vol. 88, p. 11-19
Keywords [en]
Natural Language Processing, Information extraction, Text analytics, Evaluation, Clinical informatics, Mental Health Informatics, Epidemiology, Public Health
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-247860DOI: 10.1016/j.jbi.2018.10.005ISI: 000460600200002PubMedID: 30368002Scopus ID: 2-s2.0-85056235308OAI: oai:DiVA.org:kth-247860DiVA, id: diva2:1299096
Note

QC 20190326

Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Velupillai, Sumithra

Search in DiVA

By author/editor
Velupillai, SumithraRoberts, AngusMorley, KatherineDowns, Johnny
By organisation
Theoretical Computer Science, TCS
In the same journal
Journal of Biomedical Informatics
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 8 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf