Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Zuo, Robinson Lauren, Whelan Robert, Jollans Lee, Wang Zijian, Nees Frauke, Chu Congying, Bobou Marina, Du Dongping, Cristea Ilinca, Banaschewski Tobias, Barker Gareth J, Bokde Arun L W, Grigis Antoine, Garavan Hugh, Heinz Andreas, Brühl Rüdiger, Martinot Jean-Luc, Martinot Marie-Laure Paillère, Artiges Eric, Orfanos Dimitri Papadopoulos, Poustka Luise, Hohmann Sarah, Millenet Sabina, Fröhner Juliane H, Smolka Michael N, Vaidya Nilakshi, Walter Henrik, Winterer Jeanne, Broulidakis M John, van Noort Betteke Maria, Stringaris Argyris, Penttilä Jani, Grimmer Yvonne, Insensee Corinna, Becker Andreas, Zhang Yuning, King Sinead, Sinclair Julia, Schumann Gunter, Schmidt Ulrike, Desrivières Sylvane
Аннотация
METHODS: Case-control samples (aged 18-25 years), including participants with Anorexia Nervosa (AN), Bulimia Nervosa (BN), MDD, AUD, and matched controls, were used for diagnostic classification. For risk prediction, we used a longitudinal population-based sample (IMAGEN study), assessing adolescents at ages 14, 16 and 19. Regularized logistic regression models incorporated broad data domains spanning psychopathology, personality, cognition, substance use, and environment.
RESULTS: The classification of EDs was highly accurate, even when excluding body mass index from the analysis. 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. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. 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. In the longitudinal population sample, the models exhibited moderate performance in predicting the development of future ED symptoms (0.71 [0.67-0.75]), depressive symptoms (0.64 [0.60-0.68]), and harmful drinking (0.67 [0.64-0.70]).
CONCLUSIONS: Our findings demonstrate the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.