Psychiatry

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

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Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesiz...

Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes.

Machine learning has been suggested as a means of identifying individuals at greatest risk for hospi...

A machine learning approach to modeling PTSD and difficulties in emotion regulation.

Despite evidence for the association between emotion regulation difficulties and posttraumatic stres...

Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

The prospective identification of children likely to develop schizophrenia is a vital tool to suppor...

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 on...

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.

BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-h...

Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.

BACKGROUND: Natural language processing (NLP) tools can facilitate the extraction of biomedical conc...

Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.

BACKGROUND: Recent studies have demonstrated that passive smartphone and wearable sensor data collec...

The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood.

BACKGROUND: Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial disease bur...

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 treat...

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 ...

Artificial Intelligence in mental health and the biases of language based models.

BACKGROUND: The rapid integration of Artificial Intelligence (AI) into the healthcare field has occu...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and i...

Detection of eye contact with deep neural networks is as accurate as human experts.

Eye contact is among the most primary means of social communication used by humans. Quantification o...

A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter.

BACKGROUND: Emotions after surviving cancer can be complicated. The survivors may have gained new st...

Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls.

OBJECTIVES: While theoretical models link obsessive-compulsive disorder (OCD) with executive functio...

Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.

The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactoria...

Predicting hospitalization following psychiatric crisis care using machine learning.

BACKGROUND: Accurate prediction models for whether patients on the verge of a psychiatric criseis ne...

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