[HTML][HTML] Machine learning for medical imaging: methodological failures and recommendations for the future
G Varoquaux, V Cheplygina - NPJ digital medicine, 2022 - nature.com
Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …
health. However, a number of systematic challenges are slowing down the progress of the …
Navigating the pitfalls of applying machine learning in genomics
The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …
[HTML][HTML] ADHD: Current concepts and treatments in children and adolescents
R Drechsler, S Brem, D Brandeis, E Grünblatt… - …, 2020 - thieme-connect.com
Attention deficit hyperactivity disorder (ADHD) is among the most frequent disorders within
child and adolescent psychiatry, with a prevalence of over 5%. Nosological systems, such as …
child and adolescent psychiatry, with a prevalence of over 5%. Nosological systems, such as …
[HTML][HTML] Deep learning for small and big data in psychiatry
G Koppe, A Meyer-Lindenberg… - …, 2021 - nature.com
Psychiatry today must gain a better understanding of the common and distinct
pathophysiological mechanisms underlying psychiatric disorders in order to deliver more …
pathophysiological mechanisms underlying psychiatric disorders in order to deliver more …
[HTML][HTML] Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
Spatial–temporal co-attention learning for diagnosis of mental disorders from resting-state fMRI data
Neuroimaging techniques have been widely adopted to detect the neurological brain
structures and functions of the nervous system. As an effective noninvasive neuroimaging …
structures and functions of the nervous system. As an effective noninvasive neuroimaging …
Annual Research Review: Translational machine learning for child and adolescent psychiatry
D Dwyer, N Koutsouleris - Journal of Child Psychology and …, 2022 - Wiley Online Library
Children and adolescents could benefit from the use of predictive tools that facilitate
personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been …
personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been …
[HTML][HTML] Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Brain imaging research enjoys increasing adoption of supervised machine learning for
single-participant disease classification. Yet, the success of these algorithms likely depends …
single-participant disease classification. Yet, the success of these algorithms likely depends …
Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review
Neurodevelopmental disorders–including attention‐deficit/hyperactivity disorder (ADHD),
autism spectrum disorder, communication disorders, intellectual disability, motor disorders …
autism spectrum disorder, communication disorders, intellectual disability, motor disorders …
[HTML][HTML] Altered neural flexibility in children with attention-deficit/hyperactivity disorder
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common
neurodevelopmental disorders of childhood, and is often characterized by altered executive …
neurodevelopmental disorders of childhood, and is often characterized by altered executive …