Psychiatry

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.

Predicting individual clinical trajectories of depression with generative embedding.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, rel...

Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study.

INTRODUCTION: Subjective tinnitus (ST) and hyperacusis (HA) are common auditory symptoms that may become incapacitating in a subgroup of patients who ...

PANDA: Prioritization of autism-genes using network-based deep-learning approach.

Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of ...

Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach.

BACKGROUND: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study wa...

Diagnosing schizophrenia with network analysis and a machine learning method.

OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the...

Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.

IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia.

Searching for the Big Pictures.

My goal in searching for the big pictures is to discover novel ways of organizing information in psychology that will have both theoretical and practi...

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory m...

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches.

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics,...

Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.

Because depression has high prevalence and cause enduring disability, it is important to predict onset of depression among community dwelling adults. ...

'AI gone mental': engagement and ethics in data-driven technology for mental health.

Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.

Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype ...

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.

BACKGROUND: Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention...

Nonintrusive Monitoring of Mental Fatigue Status Using Epidermal Electronic Systems and Machine-Learning Algorithms.

Mental fatigue, characterized by subjective feelings of "tiredness" and "lack of energy", can degrade individual performance in a variety of situation...

Identifying epilepsy psychiatric comorbidities with machine learning.

OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect su...

Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits.

Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communic...

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly diff...

Positive Emotions, More Than Anxiety or Other Negative Emotions, Predict Willingness to Interact With Robots.

Like early work on human intergroup interaction, previous research on people's willingness to interact with robots has focused mainly on effects of an...

Improving the detection of autism spectrum disorder by combining structural and functional MRI information.

Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as res...