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Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.
Bishop-Fitzpatrick, Lauren; Movaghar, Arezoo; Greenberg, Jan S; Page, David; DaWalt, Leann S; Brilliant, Murray H; Mailick, Marsha R.
Afiliação
  • Bishop-Fitzpatrick L; Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI, 53705.
  • Movaghar A; School of Social Work, University of Wisconsin-Madison, 1350 University Ave, Madison, WI, 53706.
  • Greenberg JS; Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI, 53705.
  • Page D; Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI, 53705.
  • DaWalt LS; School of Social Work, University of Wisconsin-Madison, 1350 University Ave, Madison, WI, 53706.
  • Brilliant MH; Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792.
  • Mailick MR; Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI, 53705.
Autism Res ; 11(8): 1120-1128, 2018 08.
Article em En | MEDLINE | ID: mdl-29734508
ABSTRACT
Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a machine learning algorithm to characterize diagnostic patterns in decedents with ASD and matched decedent community controls. Participants were 91 decedents with ASD and 6,186 sex and birth year matched decedent community controls who had died since 1979, the majority of whom were middle aged or older adults at the time of their death. We analyzed all ICD-9 codes, V-codes, and E-codes available in the electronic health record and Elixhauser comorbidity categories associated with those codes. Diagnostic patterns distinguished decedents with ASD from decedent community controls with 75% sensitivity and 94% specificity solely based on their lifetime ICD-9 codes, V-codes, and E-codes. Decedents with ASD had higher rates of most conditions, including cardiovascular disease, motor problems, ear problems, urinary problems, digestive problems, side effects from long-term medication use, and nonspecific lab tests and encounters. In contrast, decedents with ASD had lower rates of cancer. Findings suggest distinctive lifetime diagnostic patterns among decedents with ASD and highlight the need for more research on health outcomes across the lifespan as the population of individuals with ASD ages. As a large wave of individuals with ASD diagnosed in the 1990s enters adulthood and middle age, knowledge about lifetime health problems will become increasingly important for care and prevention efforts. Autism Res 2018, 11 1120-1128. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY

SUMMARY:

This study looked at patterns of lifetime health problems to find differences between people with autism who had died and community controls who had died. People with autism had higher rates of most health problems, including cardiovascular, urinary, respiratory, digestive, and motor problems, in their electronic health records. They also had lower rates of cancer. More research is needed to understand these potential health risks as a large number of individuals with autism enter adulthood and middle age.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença Crônica / Registros Eletrônicos de Saúde / Transtorno do Espectro Autista / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença Crônica / Registros Eletrônicos de Saúde / Transtorno do Espectro Autista / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article