[article]
| Titre : |
Commentary to ?Translational machine learning for child and adolescent psychiatry? |
| Type de document : |
Texte imprimé et/ou numérique |
| Auteurs : |
Christos DAVATZIKOS, Auteur ; Theodore D. SATTERTHWAITE, Auteur |
| Article en page(s) : |
p.444-446 |
| Langues : |
Anglais (eng) |
| Index. décimale : |
PER Périodiques |
| Résumé : |
In this commentary on ?Translational Machine Learning for Child and Adolescent Psychiatry,? by Dwyer and Koutsouleris, we summarize some of the main points made by the authors, which highlight the importance of emerging applications of machine learning for psychiatric disorders in youth but also emphasize principles of good practice. We also offer complementary insights regarding large-scale training, harmonization, and the ability of these artificial intelligence models to adapt to new datasets, which is critical for their stability across imaging centers, and hence for their widespread clinical adoption. |
| En ligne : |
https://doi.org/10.1111/jcpp.13593 |
| Permalink : |
https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=475 |
in Journal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.444-446
[article] Commentary to ?Translational machine learning for child and adolescent psychiatry? [Texte imprimé et/ou numérique] / Christos DAVATZIKOS, Auteur ; Theodore D. SATTERTHWAITE, Auteur . - p.444-446. Langues : Anglais ( eng) in Journal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.444-446
| Index. décimale : |
PER Périodiques |
| Résumé : |
In this commentary on ?Translational Machine Learning for Child and Adolescent Psychiatry,? by Dwyer and Koutsouleris, we summarize some of the main points made by the authors, which highlight the importance of emerging applications of machine learning for psychiatric disorders in youth but also emphasize principles of good practice. We also offer complementary insights regarding large-scale training, harmonization, and the ability of these artificial intelligence models to adapt to new datasets, which is critical for their stability across imaging centers, and hence for their widespread clinical adoption. |
| En ligne : |
https://doi.org/10.1111/jcpp.13593 |
| Permalink : |
https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=475 |
|
Commentary to ?Translational machine learning for child and adolescent psychiatry? in Journal of Child Psychology and Psychiatry, 63-4 (April 2022)
