id:"swepub:oai:DiVA.org:kth-246942"
Sökning: id:"swepub:oai:DiVA.org:kth-246942" > Detection of Suicid...
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03025naa a2200373 4500 | |
| 001 | oai:DiVA.org:kth-246942 | |
| 003 | SwePub | |
| 008 | 190619s2017 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2469422 URI |
| 040 | a (SwePub)kth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Downs, J.4 aut |
| 245 | 1 0 | a Detection of Suicidality in Adolescents with Autism Spectrum Disorders :b Developing a Natural Language Processing Approach for Use in Electronic Health Records |
| 264 | 1 | b NLM (Medline),c 2017 |
| 338 | a print2 rdacarrier | |
| 500 | a QC 20190619 | |
| 520 | a 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. | |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Medicinsk bioteknologix Biomedicinsk laboratorievetenskap/teknologi0 (SwePub)304022 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Medical Biotechnologyx Biomedical Laboratory Science/Technology0 (SwePub)304022 hsv//eng |
| 650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Psykiatri0 (SwePub)302152 hsv//swe |
| 650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Psychiatry0 (SwePub)302152 hsv//eng |
| 700 | 1 | a Velupillai, Sumithrau KTH,Teoretisk datalogi, TCS4 aut0 (Swepub:kth)u1d8ni1i |
| 700 | 1 | a George, G.4 aut |
| 700 | 1 | a Holden, R.4 aut |
| 700 | 1 | a Kikoler, M.4 aut |
| 700 | 1 | a Dean, H.4 aut |
| 700 | 1 | a Fernandes, A.4 aut |
| 700 | 1 | a Dutta, R.4 aut |
| 710 | 2 | a KTHb Teoretisk datalogi, TCS4 org |
| 773 | 0 | t AMIA Annual Symposium Proceedingsd : NLM (Medline)g 2017, s. 641-649q 2017<641-649x 1942-597X |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246942 |
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