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030 Automating clinical audit in neurology

Journal of neurology, neurosurgery and psychiatry, 2019-12, Vol.90 (12), p.A18-A18 [Peer Reviewed Journal]

Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ. ;2019 Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ. ;ISSN: 0022-3050 ;EISSN: 1468-330X ;DOI: 10.1136/jnnp-2019-ABN-2.57

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  • Title:
    030 Automating clinical audit in neurology
  • Author: Middleton, R ; Lacey, A ; Pickrell, WO ; Fonferfko-Shadrach, B ; Pearson, O ; Marta, M ; McDonnell, G ; Thompson, SE ; Ford, DV ; Nicholas, R
  • Subjects: Amyotrophic lateral sclerosis ; Audits ; Automation ; Brain research ; Hospitals ; Immunoglobulins ; Motor neurone disease ; Neurology ; Neurosciences
  • Is Part Of: Journal of neurology, neurosurgery and psychiatry, 2019-12, Vol.90 (12), p.A18-A18
  • Description: BackgroundExtracting data from healthcare records is essential for clinical and research purposes but can be labour and time intensive. We developed a natural language processing (NLP) application to automatically extract meaningful data from routinely-generated multiple sclerosis (MS) clinic letters.MethodWe developed the system using the open source platform GATE (General Architecture for Text Engineering) and a training set of 100 manually annotated MS clinic letters. The system extracts information from each clinic letter including: MS diagnosis and type, Extended Disability Status Scale (EDSS) score, current and previous Disease Modifying Therapies (DMT), walking distance, and MRI information.For initial validation, we used 250 MS clinic letters. We compared the systems performance in extracting MS diagnosis, EDSS score and current and previous DMTs with human annotation. We recorded precision (proportion of extracted items that are accurate), recall (proportion of items that are extracted) and F1-score (harmonic mean of precision and recall).Results(see poster)ConclusionNLP can be used to automatically extract specific information from MS clinic letters and has the potential to transform clinical practice and research at a large scale.
  • Publisher: London: BMJ Publishing Group LTD
  • Language: English
  • Identifier: ISSN: 0022-3050
    EISSN: 1468-330X
    DOI: 10.1136/jnnp-2019-ABN-2.57
  • Source: ProQuest One Psychology
    ProQuest Central
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