AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Bipolar Disorder

Showing 51 to 60 of 83 articles

Clear Filters

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

F1000Research
We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatmen...

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal of affective disorders
BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine lea...

A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder.

Translational psychiatry
Mobile technologies offer new opportunities for prospective, high resolution monitoring of long-term health conditions. The opportunities seem of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mo...

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

Psychiatry research
This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar dep...

Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders.

Statistics in medicine
Feature selection is an important initial step of exploratory analysis in biomedical studies. Its main objective is to eliminate the covariates that are uncorrelated with the outcome. For highly correlated covariates, traditional feature selection me...

Borderline Personality Features in Inpatients with Bipolar Disorder: Impact on Course and Machine Learning Model Use to Predict Rapid Readmission.

Journal of psychiatric practice
BACKGROUND: Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD) also met criteria for the other diagnosis. Yet limited data are available concerning the potential...

Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.

Schizophrenia research
BACKGROUND: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReH...

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

Bipolar disorders
OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learnin...

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Scientific reports
Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP)...

Prediction of lithium response using clinical data.

Acta psychiatrica Scandinavica
OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical ma...