Bipolar Disorder

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters.

The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" ...

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and con...

Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls.

Neuroimaging data driven machine learning based predictive modeling and pattern recognition has been attracted strongly attention in biomedical scienc...

Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk...

Pattern classification as decision support tool in antipsychotic treatment algorithms.

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics duri...

Associated factors of white matter hyperintensity volume: a machine-learning approach.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we...

Leveraging digital media data for pharmacovigilance.

The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharma...

Let's not be indifferent about robots: Neutral ratings on bipolar measures mask ambivalence in attitudes towards robots.

Ambivalence, the simultaneous experience of both positive and negative feelings about one and the same attitude object, has been investigated within p...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...

Lithium-Associated Hyperparathyroidism Followed by Catatonia.

OBJECTIVE: To familiarize the medical community with the less common adverse effects of lithium on parathyroid function, we present a case of lithium-...

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preempti...

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been con...

Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.

Previous work using logistic regression suggests that cognitive control-related frontoparietal activation in early psychosis can predict symptomatic i...

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM).

Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. P...

Robot-assisted extraperitoneal para-aortic lymphadenectomy (RAePAL) performed with the bipolar cutting method.

OBJECTIVE: In comparison with laparoscopic transperitoneal para-aortic lymphadenectomy, the advantages of laparoscopic extraperitoneal para-aortic lym...

Surgical technique for mesorectal division during robot-assisted laparoscopic tumor-specific mesorectal excision (TSME) for rectal cancer using da Vinci Si surgical system: the simple switching technique (SST).

In a narrow pelvic cavity, performing sufficient tumor-specific mesorectal excision (TSME) is difficult. Even in robot-assisted laparoscopic surgery (...

A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.

BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide,...

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as ...

Can machine learning be useful as a screening tool for depression in primary care?

Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mentio...

Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks.

Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functi...