AIMC Topic: Bipolar Disorder

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Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (MDD) and bipolar disorder (BD), but accurate prediction of treatment response remains a challenge. Th...

Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.

Brain and behavior
BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter v...

A highly scalable deep learning language model for common risks prediction among psychiatric inpatients.

BMC medicine
BACKGROUND: There is a lack of studies exploring the performance of Transformers-based language models in common risks assessment among psychiatric inpatients. We aim to develop a scalable risk assessment model using multidimensional textualized data...

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

JAMA psychiatry
IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.

Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

Science progress
INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. From this perspective, it is noteworthy that formal thought disorder has historically been considered p...

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

JAMA psychiatry
IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a...

Making the most of errors: Utilizing erroneous classifications generated by machine-learning models of neuroimaging data to capture disorder heterogeneity.

Journal of psychopathology and clinical science
Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors...

Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses.

Harvard review of psychiatry
BACKGROUND: Digital phenotyping is the use of data from smartphones and wearables collected in situ for capturing a digital expression of human behaviors. Digital phenotyping techniques can be used to analyze both passively (e.g., sensor) and activel...

Convolutional Neural Network Visualization for Identification of Risk Genes in Bipolar Disorder.

Current molecular medicine
BACKGROUND: Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated.

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...