Psychiatry

Is It Ethical to Use Prognostic Estimates from Machine Learning to Treat Psychosis?

Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary examines the following ethical questions about machine learning in a case of a patient with new onset psychosis: (1) When is clinical innovation ethically acceptable? (2) How should clinicians communic...

Should Artificial Intelligence Augment Medical Decision Making? The Case for an Autonomy Algorithm.

A significant proportion of elderly and psychiatric patients do not have the capacity to make health care decisions. We suggest that machine learning technologies could be harnessed to integrate data mined from electronic health records (EHRs) and social media in order to estimate the confidence of the prediction that a patient would consent to a given treatment. We call this process, which takes ...

The Elephant in the Room: Outbreak of Carfentanil Deaths in Minnesota and the Importance of Multiagency Collaboration.

Over a three-month period in early 2017, the Hennepin County Medical Examiner's Office investigated nine apparent opioid toxicity deaths that occurred...

A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective.

Autism Spectrum Disorder (ASD) affects approximately 1% of the population and leads to impairments in social interaction, communication and restricted...

Predictive connectome subnetwork extraction with anatomical and connectivity priors.

We present a new method to identify anatomical subnetworks of the human connectome that are optimally predictive of targeted clinical variables, devel...

Machine learning in major depression: From classification to treatment outcome prediction.

AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Cur...

Prevalence of and factors related to anxiety and depression symptoms among married patients with gynecological malignancies in China.

OBJECTIVE: This study aims to investigate the prevalence of anxiety and depression among married patients with gynecological malignancies in China and...

A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.

Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetiapine are used in clinical treatment of major depre...

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.

BACKGROUND: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qu...

Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

OBJECTIVE: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supp...

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

BACKGROUND: Preventing suicides, mental disorders, and noncombat-related interpersonal violence during deployment are priorities of the US Army. We us...

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

BACKGROUND: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could ...

Structural neuroimaging as clinical predictor: A review of machine learning applications.

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain c...

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and ...

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.

Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging f...

Self-Efficacy, Poststroke Depression, and Rehabilitation Outcomes: Is There a Correlation?

BACKGROUND: The sudden live changes of stroke survivors may lead to negative psychological and behavioral outcomes, including anxiety and depressive m...

Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.

Although standard behavioral interventions for autism spectrum disorder (ASD) are effective therapies for social deficits, they face criticism for bei...

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques.

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with re...

Fast and Accurate Diagnosis of Autism (FADA): a novel hierarchical fuzzy system based autism detection tool.

The main aim of this research work was to develop and validate a novel graphical user interface based hierarchical fuzzy autism detection tool named a...

Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.

This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional...