Depression

Identifying the presence and timing of discrete mood states prior to therapy.

The present study tested a novel, person-specific method for identifying discrete mood profiles from time-series data, and examined the degree to which these profiles could be predicted by lagged mood and anxiety variables and time-based variables, including trends (linear, quadratic, cubic), cycles (12-hr, 24-hr, and 7-day), day of the week, and time of day. We analyzed ambulatory data from 45 in...

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a pop...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above curr...

Predicting individual clinical trajectories of depression with generative embedding.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients exp...

Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study.

INTRODUCTION: Subjective tinnitus (ST) and hyperacusis (HA) are common auditory symptoms that may become incapacitating in a subgroup of patients who ...

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory m...

Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.

Because depression has high prevalence and cause enduring disability, it is important to predict onset of depression among community dwelling adults. ...

Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.

Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype ...

Identifying epilepsy psychiatric comorbidities with machine learning.

OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect su...

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to me...

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a pa...

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database.

INTRODUCTION: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pa...

Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Tog...

Shall I compare thee…to a robot? An exploratory pilot study using participatory arts and social robotics to improve psychological well-being in later life.

Research suggests that the use of creative, artistic activities in later life may positively impact the psychological well-being of older adults. Soci...

Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance?

BACKGROUND: Suicide is a leading cause of death, particularly in younger persons, and this results in tremendous years of life lost.

On-site testing of multiple drugs of abuse in urine by a miniature dual-LIT mass spectrometer.

There is an increasing need for rapid and on-site detection of emerging drugs of abuse. In this work, we developed a method using a miniature dual-LIT...

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major ...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reup...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive interventi...