Psychiatry

Depression

Latest AI and machine learning research in depression for healthcare professionals.

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Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to ...

Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options.

To achieve personalized medicine, an individualized treatment strategy assigning treatment based on ...

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

BACKGROUND: Identification of individuals at increased risk for suicide is an important public healt...

Quantitative Electroencephalography in Guiding Treatment of Major Depression.

This paper reviews significant contributions to the evidence for the use of quantitative electroence...

Recent Developments in the Treatment of Depression.

The cognitive and behavioral interventions can be as efficacious as antidepressant medications and m...

Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.

There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhoo...

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However...

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts...

Machine learning in suicide science: Applications and ethics.

For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has...

BP neural network prediction model for suicide attempt among Chinese rural residents.

OBJECTIVE: This study aimed to establish and assess the Back Propagation Neural Network (BPNN) predi...

Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistenc...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severi...

Toward Automatic Risk Assessment to Support Suicide Prevention.

Suicide has been considered an important public health issue for years and is one of the main cause...

Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia.

Stable phase schizophrenia is characterized by altered patterning in tryptophan catabolites (TRYCATs...

The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review.

BACKGROUND: Machine learning techniques offer promise to improve suicide risk prediction. In the cur...

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, b...

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Many variables have been linked to different course trajectories of depression. These findings, howe...

Psychiatric stressor recognition from clinical notes to reveal association with suicide.

Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden ...

Primal world beliefs.

Beck's insight-that beliefs about one's self, future, and environment shape behavior-transformed dep...

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