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Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field / Shyam Sundar RAJAGOPALAN in Journal of Neurodevelopmental Disorders, 16 (2024)

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[article]
inJournal of Neurodevelopmental Disorders > 16 (2024)

Titre :

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field

Type de document :

texte imprimé

Auteurs :

Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur

Langues :

Anglais (eng)

Mots-clés :

Humans Machine Learning Electronic Health Records Neurodevelopmental Disorders/diagnosis/epidemiology Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology Autism Spectrum Disorder/diagnosis/epidemiology Electronic Health Record Machine Learning Neurodevelopmental Disorder Population Register for publication Not applicable. Competing interests The authors declare that there are no competing interests.

Index. décimale :

PER Périodiques

Résumé :

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early.

En ligne :

https://dx.doi.org/10.1186/s11689-024-09579-0

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