Enhancing early autism prediction based on electronic records using clinical narratives.
J Biomed Inform
; 144: 104390, 2023 08.
Article
em En
| MEDLINE
| ID: mdl-37182592
ABSTRACT
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 4â¯years) from data collected from ages 30 to 360â¯days. Our sample included 11,750 children above by age 3â¯years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30â¯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 360â¯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 30â¯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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transtorno Autístico
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Registros Eletrônicos de Saúde
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Qualitative_research
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Risk_factors_studies
Limite:
Child
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Child, preschool
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Humans
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Infant
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article