Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder ; Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.
In: ISSN: 2693-5015 ; Research Square - Preprint ; https://hal.science/hal-04468457 ; Research Square - Preprint, 2024, ⟨10.21203/rs.3.rs-3777784/v1⟩, 2024
academicJournal
Zugriff:
International audience ; Abstract This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.
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Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder ; Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.
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Autor/in / Beteiligte Person: | Desrivières, Sylvane ; Zhang, Zuo ; Robinson, Lauren ; Whelan, Robert ; Jollans, Lee ; Wang, Zijian ; Nees, Frauke ; Chu, Congying ; Bobou, Marina ; Du, Dongping ; Cristea, Ilinca ; Banaschewski, Tobias ; Barker, Gareth ; Bokde, Arun ; Grigis, Antoine ; Garavan, Hugh ; Heinz, Andreas ; Bruhl, Rudiger ; Martinot, Jean-Luc ; Paillère Martinot, Marie-Laure ; Artiges, Eric ; Papadopoulos Orfanos, Dimitri ; Poustka, Luise ; Hohmann, Sarah ; Millenet, Sabina ; Fröhner, Juliane ; Smolka, Michael ; Vaidya, Nilakshi ; Walter, Henrik ; Winterer, Jeanne ; Broulidakis, M. ; van Noort, Betteke ; Stringaris, Argyris ; Penttilä, Jani ; Grimmer, Yvonne ; Insensee, Corinna ; Becker, Andreas ; Zhang, Yuning ; King, Sinead ; Sinclair, Julia ; Schumann, Gunter ; Schmidt, Ulrike ; Orfanos, Dimitri Papadopoulos ; CB - Centre Borelli - UMR 9010 (CB) ; Service de Santé des Armées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université Paris Cité (UPCité) ; Trajectoires développementales en psychiatrie : mesures et modélisations (ERL Inserm U1299 ) ; Service de Santé des Armées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université Paris Cité (UPCité)-Service de Santé des Armées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université Paris Cité (UPCité) |
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Zeitschrift: | ISSN: 2693-5015 ; Research Square - Preprint ; https://hal.science/hal-04468457 ; Research Square - Preprint, 2024, ⟨10.21203/rs.3.rs-3777784/v1⟩, 2024 |
Veröffentlichung: | HAL CCSD ; springer, 2024 |
Medientyp: | academicJournal |
DOI: | 10.21203/rs.3.rs-3777784/v1 |
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