To investigate depression, we put forward machine learning technique as an effective and scalable method.doi:10.1007/978-981-15-5113-0_72Prashant VermaKapil SharmaGurjit Singh Walia
This diagnostic study develops a machine learning model using data from wearable digital devices to detect attention-deficit/hyperactivity disorder (ADHD)
physical activity, and stress metrics, as well as neurocognitive assays on a scalable electroencephalography (EEG) platform25, to longitudinally ascertain the predictors of depressed mood in young adults with moderate depression symptoms. We apply machine learning ...
2 and 3), i.e., contributions of the identified predictors to risk detection vary across prediction windows. In particular, as shown in Figs. 2 and 3, except for demographics, depression-, and other mental disorder-related factors, most predictors shift their importance in risk prediction ...
Machine learning-based prediction of suicidality in adolescents during the COVID-19 pandemic (2020-2021): Derivation and validation in two independent nati... R Kwon,H Lee,SK Min,... - 《Asian Journal of Psychiatry》 被引量: 0发表: 2023年 Machine Learning Based Depression, Anxiety, and ...
use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM)... F J.,Dipnall,A J.,... - 《European Psychiatry》 被引量: 2发表: 2017年 Machine-learning-enabled optimization of atomic structures us...
A Stress Recognition System Using HRV Parameters and Machine Learning Techniques -- PRESENTATION SLIDES G. Giannakakis, K. Marias, M. Tsiknakis Synthesizing Physiological and Motion Data for Stress and Meditation Detection Md Taufeeq Uddin, S. Canavan A Novel Multi-Kernel 1D Convolutional Neural ...
Early warning model of adolescent mental health based on big data and machine learning Adolescent mental health issues like depression and anxiety are rising among adolescents worldwide, leading to serious long-term consequences if left untre... Z Zhang - 《Soft Computing A Fusion of Foundations ...
Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With DepressionA Prespecified Secondary Analysis of a Randomized Clinical Trial Pranav Rajpurkar, MS1;Jingbo Yang, BS1;Nathan Dass, MS1;et al...
Using random forests models in this large sample of college students we found that four main baseline variables predicted STB at 12-month: suicidal thoughts at baseline, trait anxiety, depression symptoms, and self-esteem. The model including these variables showed good predictive performance (AUC ...