Nina de Lacy
Dados Biográficos
| ID | 7524425 |
|---|---|
| NOME | Nina de Lacy |
| PRENOMES | Nina |
| SOBRENOME | de Lacy |
| ASSINATURA | DE LACY N |
| AFILIAÇÕES | University of Utah |
| ORCID | 0000-0002-4574-3604 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2023 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI
Each disorder had a specific set of predictors, though parent problem behavioral traits and sleep disturbances represented cross-cutting themes. Additional computational experiments revealed that psychosocial predictors were more important to predicting early adolescent internalizing disorders than cognitive, neural or biological factors and generated models with better performance. Future work, including replication in additional datasets, will …
Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy
Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders wi…
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Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy
Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders wi…
Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI
Each disorder had a specific set of predictors, though parent problem behavioral traits and sleep disturbances represented cross-cutting themes. Additional computational experiments revealed that psychosocial predictors were more important to predicting early adolescent internalizing disorders than cognitive, neural or biological factors and generated models with better performance. Future work, including replication in additional datasets, will …
Psychosocial (2 obras) · Anxiety (1 obras) · Attention Deficit Hyperactivity Disorder (1 obras) · Child and Adolescent Psychosocial and Emotional Development (1 obras) · Clinical Psychology (1 obras) · Clinical Psychology (1 obras) · Cognitive psychology (1 obras) · Conduct disorder (1 obras) · Deep learning (1 obras) · Digital Mental Health Interventions (1 obras)