AIMC Topic: Psychiatric Status Rating Scales
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Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach.
Behavioural neurology
Jun 22, 2020
The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and ...
A social robot intervention on depression, loneliness, and quality of life for Taiwanese older adults in long-term care.
International psychogeriatrics
Apr 14, 2020
OBJECTIVES: To investigate the effect of a social robot intervention on depression, loneliness, and quality of life of older adults in long-term care (LTC) and to explore participants' experiences and perceptions after the intervention.
A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.
Asian journal of psychiatry
Feb 26, 2020
BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy...
Identifying epilepsy psychiatric comorbidities with machine learning.
Acta neurologica Scandinavica
Jan 22, 2020
OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classif...
Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.
European child & adolescent psychiatry
Dec 23, 2019
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shri...
Machine learning discovery of longitudinal patterns of depression and suicidal ideation.
PloS one
Sep 20, 2019
BACKGROUND AND AIM: Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide death. Few empirical data are available regarding the temporal correlation between depression symptom...
Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia.
International journal of molecular sciences
Aug 29, 2019
Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysiology is largely unknown, immune-inflammatory pathways may be involved. We examined serum interleuki...
Identifying schizophrenia subgroups using clustering and supervised learning.
Schizophrenia research
Aug 24, 2019
Schizophrenia has a 1% incidence rate world-wide and those diagnosed present with positive (e.g. hallucinations, delusions), negative (e.g. apathy, asociality), and cognitive symptoms. However, both symptom burden and associated brain alterations are...
Outcome measurement in mental health services: insights from symptom networks.
BMC psychiatry
Jun 28, 2019
BACKGROUND: In mental health, outcomes are currently measured by changes of individual scores. However, such an analysis on individual scores does not take into account the interaction between symptoms, which could yield crucial information while inv...
Artificial Intelligence based facial recognition for Mood Charting among men on life style modification and it's correlation with cortisol.
Asian journal of psychiatry
May 11, 2019
UNLABELLED: Today, clinicians and researchers believe that mood disorders in children and adolescents remain one of the most under diagnosed mental health problems. Mood disorders in adolescents also put them at risk for other conditions that may per...
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