期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 88, 期 -, 页码 11-19出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2018.10.005
关键词
Natural Language Processing; Information extraction; Text analytics; Evaluation; Clinical informatics; Mental Health Informatics; Epidemiology; Public Health
资金
- European Science Foundation (ESF)
- Swedish Research Council [2015-00359]
- Marie Sklodowska Curie Actions [INCA 600398]
- Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation
- Academy of Medical Sciences
- Medical Research Council (MRC) Clinical Research Training Fellowship [MR/L017105/1]
- Wellcome Trust Seed Award in Science [109823/Z/15/Z]
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
- National Institute for Health Research University College London Hospitals Biomedical Research Centre
- NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Bart's Health NHS Trust
- Alan Turing Institute
- EPSRC [EP/N027280/1] Funding Source: UKRI
- MRC [MC_PC_17214, MR/L017105/1] Funding Source: UKRI
- Wellcome Trust [109823/Z/15/Z] Funding Source: Wellcome Trust
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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