Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances
Year: 2018
Type: article
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 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.
Institutions: King's College London, KTH Royal Institute of Technology, Australian National University, Commonwealth Scientific and Industrial Research Organisation, University of Turku, University of Canberra, Data61, University of Warwick, The Alan Turing Institute, University College London Hospitals NHS Foundation Trust, University College London, The University of Melbourne, Camden and Islington NHS Foundation Trust, South London and Maudsley NHS Foundation Trust, University of Utah
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,760
Funders: South London and Maudsley NHS Foundation Trust, Wellcome Trust, Alan Turing Institute, National Institute for Health and Care Research, Academy of Medical Sciences, King's College London, University College London, European Science Foundation, Commonwealth Scientific and Industrial Research Organisation, Vetenskapsrådet, Institut National Du Cancer, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Medical Research Council, Engineering and Physical Sciences Research Council, European Social Fund
Awards: G1525809646, G1998218567, G3456956060, MeDESTO - Measuring Duration of Untreated Psychosis by Extraction of Symptom and Treatment Onset from mental health records using language technology, ASTERISK: Addiction Services Treatment Evaluation of RISK., Healtex: UK Healthcare Text Analytics Research Network, G6789374306, G6868616705, KCL Application for a Mental Health Data Pathfinder award, ASTERISK: Addiction Services Treatment Evaluation of RISK., What are the clinical and educational risk factors for poor academic outcomes in children with Autistic Spectrum Disorder?, NIHR Senior Investigator Award, G8750378554
OpenAlex ID: W2898155085