Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach

Frontiers in Computational Neuroscience

卷:15

Inmaculada Peñuelas-Calvo¹  Enrique Baca-García²  Juan C. Laria³  David Delgado-Gómez⁴  Rosa E. Lillo⁴ 

[1] Department of Psychiatry, Fundación Jiménez Díaz Hospital, Madrid, Spain;

[2] Department of Psychiatry, Nimes University Hospital, Nimes, France;

[3] Department of Statistics, University Carlos III of Madrid, Madrid, Spain;

[4] Santander Big Data Institute, Universidad Carlos III de Madrid, Madrid, Spain;

关键词: deep learning;    lasso;    feature selection;    interpretability;    ADHD;   

DOI  :  10.3389/fncom.2021.674028

来源: DOAJ

【 摘 要 】

The deep lasso algorithm (dlasso) is introduced as a neural version of the statistical linear lasso algorithm that holds benefits from both methodologies: feature selection and automatic optimization of the parameters (including the regularization parameter). This last property makes dlasso particularly attractive for feature selection on small samples. In the two first conducted experiments, it was observed that dlasso is capable of obtaining better performance than its non-neuronal version (traditional lasso), in terms of predictive error and correct variable selection. Once that dlasso performance has been assessed, it is used to determine whether it is possible to predict the severity of symptoms in children with ADHD from four scales that measure family burden, family functioning, parental satisfaction, and parental mental health. Results show that dlasso is able to predict parents' assessment of the severity of their children's inattention from only seven items from the previous scales. These items are related to parents' satisfaction and degree of parental burden.

【 授权许可】

Unknown