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Enhancing early autism prediction based on electronic records using clinical narratives.

Publication ,  Journal Article
Chen, J; Engelhard, M; Henao, R; Berchuck, S; Eichner, B; Perrin, EM; Sapiro, G; Dawson, G
Published in: J Biomed Inform
August 2023

Recent work has shown that predictive models can be applied to structured electronic health record (EHR) data to stratify autism likelihood from an early age (<1 year). Integrating clinical narratives (or notes) with structured data has been shown to improve prediction performance in other clinical applications, but the added predictive value of this information in early autism prediction has not yet been explored. In this study, we aimed to enhance the performance of early autism prediction by using both structured EHR data and clinical narratives. We built models based on structured data and clinical narratives separately, and then an ensemble model that integrated both sources of data. We assessed the predictive value of these models from Duke University Health System over a 14-year span to evaluate ensemble models predicting later autism diagnosis (by age 4 years) from data collected from ages 30 to 360 days. Our sample included 11,750 children above by age 3 years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30 days achieved 46.8% sensitivity (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive predictive value (PPV) at high (90%) specificity (CI: 2.0%, 33.1%), and AUC4 (with at least 4-year follow-up for controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by 360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%), and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%), and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show that incorporating clinical narratives in early autism prediction achieved promising accuracy by age 30 days, outperforming models based on structured data only. Furthermore, findings suggest that additional features learned from clinician narratives might be hypothesis generating for understanding early development in autism.

Duke Scholars

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Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

August 2023

Volume

144

Start / End Page

104390

Location

United States

Related Subject Headings

  • Predictive Value of Tests
  • Narration
  • Medical Informatics
  • Infant
  • Humans
  • Electronics
  • Electronic Health Records
  • Child, Preschool
  • Child
  • Biomedical Engineering
 

Citation

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Chen, J., Engelhard, M., Henao, R., Berchuck, S., Eichner, B., Perrin, E. M., … Dawson, G. (2023). Enhancing early autism prediction based on electronic records using clinical narratives. J Biomed Inform, 144, 104390. https://doi.org/10.1016/j.jbi.2023.104390
Chen, Junya, Matthew Engelhard, Ricardo Henao, Samuel Berchuck, Brian Eichner, Eliana M. Perrin, Guillermo Sapiro, and Geraldine Dawson. “Enhancing early autism prediction based on electronic records using clinical narratives.J Biomed Inform 144 (August 2023): 104390. https://doi.org/10.1016/j.jbi.2023.104390.
Chen J, Engelhard M, Henao R, Berchuck S, Eichner B, Perrin EM, et al. Enhancing early autism prediction based on electronic records using clinical narratives. J Biomed Inform. 2023 Aug;144:104390.
Chen, Junya, et al. “Enhancing early autism prediction based on electronic records using clinical narratives.J Biomed Inform, vol. 144, Aug. 2023, p. 104390. Pubmed, doi:10.1016/j.jbi.2023.104390.
Chen J, Engelhard M, Henao R, Berchuck S, Eichner B, Perrin EM, Sapiro G, Dawson G. Enhancing early autism prediction based on electronic records using clinical narratives. J Biomed Inform. 2023 Aug;144:104390.
Journal cover image

Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

August 2023

Volume

144

Start / End Page

104390

Location

United States

Related Subject Headings

  • Predictive Value of Tests
  • Narration
  • Medical Informatics
  • Infant
  • Humans
  • Electronics
  • Electronic Health Records
  • Child, Preschool
  • Child
  • Biomedical Engineering