Psychiatry

Predicting women with depressive symptoms postpartum with machine learning methods.

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine le...

A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis.

Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched theori...

Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery.

Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Rob...

Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University.

There is increasing use of psychotherapy apps in mental health care. This mixed methods pilot study aimed to explore postgraduate clinical psycholog...

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis.

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autis...

The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study.

This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method ...

Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.

OBJECTIVES: We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free...

Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.

It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while...

[Artificial intelligence in psychiatry: predictive value of characteristics on MR imaging of the brain].

The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological asse...

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; howeve...

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware attention framework for relationship extraction.

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression...

How do you feel? Using natural language processing to automatically rate emotion in psychotherapy.

Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, s...

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.

BACKGROUND: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mH...

A novel method for clinical risk prediction with low-quality data.

In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constan...

Action detection using a neural network elucidates the genetics of mouse grooming behavior.

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate se...

Sparse deep neural networks on imaging genetics for schizophrenia case-control classification.

Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classificat...

HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.

The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's healt...

Reducing negative emotions in children using social robots: systematic review.

BACKGROUND: For many children, visiting the hospital can lead to a state of increased anxiety. Social robots are being explored as a possible tool to ...

Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment.

Smartphones can be used to passively assess and monitor patients' speech impairments caused by ailments such as Parkinson's disease, Traumatic Brain ...

[Diagnostics and Therapy 24/7? Artificial Intelligence as a Challenge and Opportunity in Psychiatry and Psychotherapy].

OBJECTIVE: The aim of the article is to enable a fundamental understanding of the potentials and requirements of Artificial Intelligence (AI) for psyc...