Depression

Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR phenotyping. Most STB phenotyping studies have used structured EHR data, but some are beginning to incorporate unstructured clinical text. In this study...

User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, we are developing an intervention that uses predictive analytics to inform care teams about their patients' risk of suicide attempt. We present our exp...

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities.

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM ca...

Identifying intentional injuries among children and adolescents based on Machine Learning.

BACKGROUND: Compared to other studies, the injury monitoring of Chinese children and adolescents has captured a low level of intentional injuries on a...

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.

Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the si...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...

Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy.

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental healt...

Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.

Patients with cancer often exhibit multiple co-occurring symptoms which can impact the type of treatment received, recovery, and long-term health. We ...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women'...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has ...

Determination of amphetamines, ketamine and their metabolites in hair with high-speed grinding and solid-phase microextraction followed by LC-MS.

A novel hair sample pre-treatment method based on high-speed grinding and solid-phase microextraction (SPME) had been applied for the determination of...

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hamperi...

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preempti...

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.

BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to...

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been con...

Deep learning based classification of facial dermatological disorders.

Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one ...

Interpretation of Depression Detection Models via Feature Selection Methods.

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatica...

Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.

OBJECTIVE: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI...

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second...

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

BACKGROUND: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most...