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Using Clinical Natural Language Processing for Health Outcomes Research: Overview and Actionable Suggestions for Future Advances.

Using Clinical Natural Language Processing for Health Outcomes Research: Overview and Actionable Suggestions for Future Advances.

Using Clinical Natural Language Processing for Health Outcomes Research: Overview and Actionable Suggestions for Future Advances.

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Using Clinical Natural Language Processing for Health Outcomes Research: Overview and Actionable Suggestions for Future Advances.

Velupillai S1, Suominen H2, Liakata M3, Roberts A4, Shah AD5, Morley K6, Osborn D7, Hayes J8, Stewart R9, Downs J10, Chapman W11, Dutta R12.

Author information:

  1. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK; School of Electrical Engineering and Computer Science, KTH, Stockholm, Sweden. Electronic address: velupillai@kcl.ac.uk.
  2. College of Engineering and Computer Science, The Australian National University, Data61/CSIRO, & Universities of Canberra, Australia and Turku, Finland. Electronic address: Suominen@anu.edu.au.
  3. Department of Computer Science, University of Warwick. Electronic address: liakata@warwick.ac.uk.
  4. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK. Electronic address: roberts@kcl.ac.uk.
  5. Institute of Health Informatics, University College London, UK; University College London NHS Foundation Trust. London, UK. Electronic address: anoop@doctors.org.uk.
  6. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK; Melbourne School of Population and Global Health, The University of Melbourne, Australia. Electronic address: morley@kcl.ac.uk.
  7. Division of Psychiatry, University College London, UK; Camden and Islington NHS Foundation Trust, London, UK. Electronic address: osborn@ucl.ac.uk.
  8. Division of Psychiatry, University College London, UK; Camden and Islington NHS Foundation Trust, London, UK. Electronic address: hayes@ucl.ac.uk.
  9. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK; South London and Maudsley NHS Foundation Trust, London, UK. Electronic address: stewart@kcl.ac.uk.
  10. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK; South London and Maudsley NHS Foundation Trust, London, UK. Electronic address: downs@kcl.ac.uk.
  11. Department of Biomedical Informatics, University of Utah, US.
  12. Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK; Camden and Islington NHS Foundation Trust, London, UK. Electronic address: rina.dutta@kcl.ac.uk.

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.

Copyright © 2018. Published by Elsevier Inc.

DOI: 10.1016/j.jbi.2018.10.005
PMID: 30368002