Candidate biomarkers in psychiatric disorders: state of the field
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can
aid in objectively diagnosing patients and providing individualized treatment …
aid in objectively diagnosing patients and providing individualized treatment …
An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders
We aimed to identify diagnosis-specific/transdiagnostic/transoutcome multivariable
candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an …
candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an …
A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
Importance Biological psychiatry aims to understand mental disorders in terms of altered
neurobiological pathways. However, for one of the most prevalent and disabling mental …
neurobiological pathways. However, for one of the most prevalent and disabling mental …
Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach
Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high
degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood …
degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood …
Multi-omics data integration methods and their applications in psychiatric disorders
To study mental illness and health, in the past researchers have often broken down their
complexity into individual subsystems (eg, genomics, transcriptomics, proteomics, clinical …
complexity into individual subsystems (eg, genomics, transcriptomics, proteomics, clinical …
Evidence of questionable research practices in clinical prediction models
Background Clinical prediction models are widely used in health and medical research. The
area under the receiver operating characteristic curve (AUC) is a frequently used estimate to …
area under the receiver operating characteristic curve (AUC) is a frequently used estimate to …
[HTML][HTML] Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward
AK Deisenhofer, M Barkham, ET Beierl… - Behaviour research and …, 2024 - Elsevier
Personalization of psychological therapies has always been used by clinicians and
describes all efforts to select, adjust, or modify a treatment for the individual to improve …
describes all efforts to select, adjust, or modify a treatment for the individual to improve …
Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
Background Having an appropriate sample size is important when developing a clinical
prediction model. We aimed to review how sample size is considered in studies developing …
prediction model. We aimed to review how sample size is considered in studies developing …
Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review
Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
mental disorder is recognized as a significant breakthrough in the field of psychiatry …