Depression

A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder.

Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnose...

Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.

The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a c...

Physicochemical stability of oxycodone-ketamine solutions in polypropylene syringe and polyvinyl chloride bag for patient-controlled analgesia use.

OBJECTIVES: The study evaluated the stability of three combinations of oxycodone and ketamine diluted in normal saline (NS) after storage for 7 days a...

Does an interaction exist between ketamine hydrochloride and Becton Dickinson syringes?

INTRODUCTION: An international alert from Becton Dickinson (BD) has noted the possibility of interaction between several molecules and some syringes. ...

Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.

Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recentl...

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder.

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals i...

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

BACKGROUND: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (...

Non-reward neural mechanisms in the orbitofrontal cortex.

Single neurons in the primate orbitofrontal cortex respond when an expected reward is not obtained, and behaviour must change. The human lateral orbit...

Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016).

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological da...

Reflections of Low-Income, Second-Generation Latinas About Experiences in Depression Therapy.

Depression is higher among second-generation Latinas compared with immigrants, but mental health treatment is stigmatized. Therefore, second-generatio...

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

BACKGROUND: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empi...

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

OBJECTIVE: A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However...

Does lower urine-specific gravity predict decline in renal function and hypernatremia in older adults exposed to psychotropic medications? An exploratory analysis.

BACKGROUND: Exposure to psychotropic agents, including lithium, antipsychotics and antidepressants, has been associated with nephrogenic diabetes insi...

Studying depression using imaging and machine learning methods.

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. Thes...

Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

OBJECTIVE: The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, mic...

MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs.

UNLABELLED: S-sulfenylation (S-sulphenylation, or sulfenic acid), the covalent attachment of S-hydroxyl (-SOH) to cysteine thiol, plays a significant ...

Antidepressant use in late gestation and risk of postpartum haemorrhage: a retrospective cohort study.

OBJECTIVE: To investigate the association between antidepressant use in late gestation and postpartum haemorrhage (PPH).

Rich spectrum of neural field dynamics in the presence of short-term synaptic depression.

In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represe...

Analysis of NTSB Aircraft-Assisted Pilot Suicides: 1982-2014.

On March 24, 2015, a Germanwings aircraft crashed in the Alps. The suicidal copilot killed himself and 150 others. Pilot suicide is rare, but does hap...