AIMC Topic: Mental Disorders
Clear Filters Showing 51 to 60 of 281 articles
Exploring correlates of high psychiatric inpatient utilization in Switzerland: a descriptive and machine learning analysis.
BMC psychiatry
Dec 23, 2024
BACKGROUND: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of psychiatric inpatient services. Our objective was to better understand the utilization pattern and t...
Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.
Cell
Dec 19, 2024
Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitat...
A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.
Medical engineering & physics
Dec 12, 2024
Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and participate in daily activities. It is challenging to diagnose these disorders due to a lack of reliable d...
Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review.
Computers in biology and medicine
Dec 11, 2024
BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic r...
CALLM: Enhancing Clinical Interview Analysis Through Data Augmentation With Large Language Models.
IEEE journal of biomedical and health informatics
Dec 5, 2024
The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges...
AI depictions of psychiatric diagnoses: a preliminary study of generative image outputs in Midjourney V.6 and DALL-E 3.
BMJ mental health
Dec 4, 2024
OBJECTIVE: This paper investigates how state-of-the-art generative artificial intelligence (AI) image models represent common psychiatric diagnoses. We offer key lessons derived from these representations to inform clinicians, researchers, generative...
Multimodal machine learning for language and speech markers identification in mental health.
BMC medical informatics and decision making
Nov 22, 2024
BACKGROUND: There are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal approaches to diagnose a variet...
AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.
Journal of medical Internet research
Nov 15, 2024
BACKGROUND: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observabl...
Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.
Journal of medical Internet research
Nov 13, 2024
BACKGROUND: Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnou...
Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.
Current psychiatry reports
Nov 11, 2024
PURPOSE OF REVIEW: This review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the cl...
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