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

Research output: Contribution to journalArticle

Sumithra Velupillai, Hanna Suominen, Maria Liakata, Angus Roberts, Anoop D. Shah, Katherine Morley, David Osborn, Joseph F. Hayes, Robert Stewart, Johnny Downs, Wendy Chapman, Rina Dutta

Original languageEnglish
Pages (from-to)11-19
JournalJOURNAL OF BIOMEDICAL INFORMATICS
Volume88
Early online date24 Oct 2018
DOIs
Publication statusPublished - Dec 2018

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Abstract

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 specic 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 specic 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 scientic objectives of each eld, and because of dierences 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 dening 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 signicant, but we propose more emphasis needs to be placed on
rigorous evaluation for the eld 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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