AIMC Topic: Bipolar Disorder
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Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.
Journal of psychiatric research
Dec 6, 2019
A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The g...
Prediction of lithium response using clinical data.
Acta psychiatrica Scandinavica
Nov 22, 2019
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...
Implementing machine learning in bipolar diagnosis in China.
Translational psychiatry
Nov 18, 2019
Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learnin...
Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.
Scientific reports
Oct 2, 2019
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)...
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
Sep 18, 2019
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...
Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.
Schizophrenia research
Aug 19, 2019
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...
Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders.
Statistics in medicine
Jul 15, 2019
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...
Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.
Psychiatry research
Mar 29, 2019
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...
A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder.
Translational psychiatry
Dec 13, 2018
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...
Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.
Journal of affective disorders
Nov 6, 2018
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...
Demography
Feeding and Eating Disorders
Hallucinations
Delusions
Middle Aged
Bipolar Disorder
Hospitalization
Area Under Curve
Cohort Studies
Adult
Humans
Alcoholism
ROC Curve
Age Factors
Cross-Sectional Studies
Tobacco Use Disorder
Male
Machine Learning
Substance-Related Disorders
Algorithms
Female
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