Journal
SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s41598-020-80457-0
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Funding
- Swedish Research Council [2015-00359]
- Marie Skodowska Curie Actions [INCA 600398]
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
- Medical Research Council (MRC) Mental Health Data Pathfinder Award
- NIHR Senior Investigator Award
- Medical Research Council (MRC) Health Data Research UK Fellowship [MR/S003118/1]
- Academy of Medical Sciences [SGL015/1020]
- Wellcome Trust
- MRC
- British Heart Foundation
- Arthritis Research UK
- Royal College of Physicians
- Diabetes UK
- EPSRC [EP/N027280/1] Funding Source: UKRI
- MRC [MR/S003118/1, MC_PC_17214] Funding Source: UKRI
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Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential, including the date of psychosis symptom onset in mental health domain. Using electronic health records (EHRs) and natural language processing (NLP) techniques can help identify disease onset and improve intervention outcomes.
Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively. However, disease onset information is often only available in open text fields, requiring natural language processing (NLP) techniques for automated analyses. Since this variable can be documented at different points during a patient's care, NLP methods that model clinical and temporal associations are needed. We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates. For 22/31 test patients (71%) the correct onset date was found among the top-3 NLP predictions. The proposed approach was also applied at scale, allowing an onset date to be estimated for 2483 patients.
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