Machine learning in mental health: a scoping review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
Systematic review of privacy-preserving distributed machine learning from federated databases in health care
Big data for health care is one of the potential solutions to deal with the numerous
challenges of health care, such as rising cost, aging population, precision medicine …
challenges of health care, such as rising cost, aging population, precision medicine …
Distributed deep learning networks among institutions for medical imaging
Objective Deep learning has become a promising approach for automated support for
clinical diagnosis. When medical data samples are limited, collaboration among multiple …
clinical diagnosis. When medical data samples are limited, collaboration among multiple …
Split learning for collaborative deep learning in healthcare
Shortage of labeled data has been holding the surge of deep learning in healthcare back, as
sample sizes are often small, patient information cannot be shared openly, and multi-center …
sample sizes are often small, patient information cannot be shared openly, and multi-center …
Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters
HG Schnack, RS Kahn - Frontiers in psychiatry, 2016 - frontiersin.org
In a recent review, it was suggested that much larger cohorts are needed to prove the
diagnostic value of neuroimaging biomarkers in psychiatry. While within a sample, an …
diagnostic value of neuroimaging biomarkers in psychiatry. While within a sample, an …
Making individual prognoses in psychiatry using neuroimaging and machine learning
RJ Janssen, J Mourão-Miranda, HG Schnack - … Cognitive Neuroscience and …, 2018 - Elsevier
Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the
future, as opposed to making a diagnosis, which is concerned with the current state. During …
future, as opposed to making a diagnosis, which is concerned with the current state. During …
Using structural MRI to identify bipolar disorders–13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective
biological markers, such as those based on brain imaging, could aid in clinical management …
biological markers, such as those based on brain imaging, could aid in clinical management …
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 …
Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to
detect psychosis at the individual level, the reliability of the findings is unclear due to …
detect psychosis at the individual level, the reliability of the findings is unclear due to …
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 …