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 …
Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges
Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …
support risk stratification and individualized care within psychiatry. Despite growing interest …
The promise of machine learning in predicting treatment outcomes in psychiatry
For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …
medications or psychotherapies, in order to personalize their treatment choices. There is …
Predicting risk of suicide attempts over time through machine learning
CG Walsh, JD Ribeiro… - Clinical Psychological …, 2017 - journals.sagepub.com
Traditional approaches to the prediction of suicide attempts have limited the accuracy and
scale of risk detection for these dangerous behaviors. We sought to overcome these …
scale of risk detection for these dangerous behaviors. We sought to overcome these …
Treatment selection in depression
ZD Cohen, RJ DeRubeis - Annual Review of Clinical …, 2018 - annualreviews.org
Mental health researchers and clinicians have long sought answers to the question “What
works for whom?” The goal of precision medicine is to provide evidence-based answers to …
works for whom?” The goal of precision medicine is to provide evidence-based answers to …
Artificial intelligence and suicide prevention: a systematic review of machine learning investigations
Suicide is a leading cause of death that defies prediction and challenges prevention efforts
worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means …
worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means …
Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine …
N Koutsouleris, L Kambeitz-Ilankovic… - JAMA …, 2018 - jamanetwork.com
Importance Social and occupational impairments contribute to the burden of psychosis and
depression. There is a need for risk stratification tools to inform personalized functional …
depression. There is a need for risk stratification tools to inform personalized functional …
Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm
Diabetes mellitus is a disease commonly called Diabetes. Diabetes is among the most
frequent diseases globally. This disease affects internationally with different ailments and …
frequent diseases globally. This disease affects internationally with different ailments and …
[HTML][HTML] Machine learning in healthcare
H Habehh, S Gohel - Current genomics, 2021 - ncbi.nlm.nih.gov
Abstract Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML)
technology have brought on substantial strides in predicting and identifying health …
technology have brought on substantial strides in predicting and identifying health …
Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review
Y Lee, RM Ragguett, RB Mansur, JJ Boutilier… - Journal of affective …, 2018 - Elsevier
Background No previous study has comprehensively reviewed the application of machine
learning algorithms in mood disorders populations. Herein, we qualitatively and …
learning algorithms in mood disorders populations. Herein, we qualitatively and …