Machine Learning Differentiation of Autism Spectrum Sub-Classifications.
Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can ma...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 54; no. 11; pp. 4216 - 4232 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180153908&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180153908 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Nov2024 vid: 54 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180153908 173576247 180153908 180153908 10.1007/s10803-023-06121-4 180153908 ppf: 4216 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning Differentiation of Autism Spectrum Sub-Classifications. aug: au: Thapa, R Garikipati, A Ciobanu, M Singh, NP Browning, E DeCurzio, J Barnes, G Dinenno, FA Mao, Q Das, R affil: Montera, Inc dba Forta, 548 Market St, PMB 89605, San Francisco, CA, USA sug: subj: Machine Learning Autism Spectrum Disorder Classification Autism Spectrum Disorder Diagnosis DSM Human Retrospective Design Algorithms Descriptive Statistics ab: Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum. Methods: We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data. Results: The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum. Conclusion: Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|