Citation

Downs J, Velupillai S, George G, Holden R, Kikoler M, Dean H, Fernandes A, Dutta R. AMIA Annu. Symp. Proc. 2017; 2017: 641-649.

Affiliation

South London and Maudsley NHS Foundation Trust, London, UK.

Copyright

(Copyright © 2017, American Medical Informatics Association)

DOI

unavailable

PMID

29854129

Abstract

Over 15% of young people with autism spectrum disorders (ASD) will contemplate or attempt suicide during adolescence. Yet, there is limited evidence concerning risk factors for suicidality in childhood ASD. Electronic health records (EHRs) can be used to create retrospective clinical cohort data for large samples of children with ASD. However systems to accurately extract suicidality-related concepts need to be developed so that putative models of suicide risk in ASD can be explored. We present a systematic approach to 1) adapt Natural Language Processing (NLP) solutions to screen with high sensitivity for reference to suicidal constructs in a large clinical ASD EHR corpus (230,465 documents), and 2) evaluate within a screened subset of 500 patients, the performance of an NLP classification tool for positive and negated suicidal mentions within clinical text. When evaluated, the NLP classification tool showed high system performance for positive suicidality with precision, recall, and F1 scores all > 0.85 at a document and patient level. The application therefore provides accurate output for epidemiological research into the factors contributing to the onset and recurrence of suicidality, and potential utility within clinical settings as an automated surveillance or risk prediction tool for specialist ASD services.

Language: en