Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly
prevalent and impairing problems, but frequently go undetected, leading to substantial …
prevalent and impairing problems, but frequently go undetected, leading to substantial …
Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm
Background As depression is the leading cause of disability worldwide, large-scale surveys
have been conducted to establish the occurrence and risk factors of depression. However …
have been conducted to establish the occurrence and risk factors of depression. However …
Using machine learning-based analysis for behavioral differentiation between anxiety and depression
Anxiety and depression are distinct—albeit overlapping—psychiatric diseases, currently
diagnosed by self-reported-symptoms. This research presents a new diagnostic …
diagnosed by self-reported-symptoms. This research presents a new diagnostic …
Deep learning in mental health outcome research: a scoping review
Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment
Informatics paradigms for brain and mental health research have seen significant advances
in recent years. These developments can largely be attributed to the emergence of new …
in recent years. These developments can largely be attributed to the emergence of new …
An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …
Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry
AMY Tai, A Albuquerque, NE Carmona… - Artificial intelligence in …, 2019 - Elsevier
Introduction Machine learning capability holds promise to inform disease models, the
discovery and development of novel disease modifying therapeutics and prevention …
discovery and development of novel disease modifying therapeutics and prevention …
[HTML][HTML] Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier …
WA van Eeden, C Luo, AM van Hemert, IVE Carlier… - Psychiatry …, 2021 - Elsevier
Background Predicting the onset and course of mood and anxiety disorders is of clinical
importance but remains difficult. We compared the predictive performances of traditional …
importance but remains difficult. We compared the predictive performances of traditional …
Machine learning-based predictive modeling of depression in hypertensive populations
We aimed to develop prediction models for depression among US adults with hypertension
using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms …
using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms …
Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011–2018
C Zhang, X Chen, S Wang, J Hu, C Wang, X Liu - Psychiatry Research, 2021 - Elsevier
Depression is one of the most common mental health problems in middle-aged and elderly
people. The establishment of risk factor-based depression risk assessment model is …
people. The establishment of risk factor-based depression risk assessment model is …
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