A clinical risk stratification tool for predicting treatment resistance in major depressive disorder
about
Cognitive-Behavioural Analysis System of Psychotherapy (CBASP), a drug, or their combination: differential therapeutics for persistent depressive disorder: a study protocol of an individual participant data network meta-analysisAn Objective Screening Method for Major Depressive Disorder Using Logistic Regression Analysis of Heart Rate Variability Data Obtained in a Mental Task Paradigm.Quest for biomarkers of treatment-resistant depression: shifting the paradigm toward risk.Use of large data sets and the future of personalized treatment.Individual patient data meta-analysis of combined treatments versus psychotherapy (with or without pill placebo), pharmacotherapy or pill placebo for adult depression: a protocolPretreatment brain states identify likely nonresponse to standard treatments for depression.Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial.Rare copy number variation in treatment-resistant major depressive disorderComputational neuroscience approach to biomarkers and treatments for mental disorders.Clinical predictors of ketamine response in treatment-resistant major depression.Neurocognitive effects of ketamine and association with antidepressant response in individuals with treatment-resistant depression: a randomized controlled trialMultiple risk factors predict recurrence of major depressive disorder in women.The establishment of the objective diagnostic markers and personalized medical intervention in patients with major depressive disorder: rationale and protocol.Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.Machine learning, statistical learning and the future of biological research in psychiatry.Abandoning personalization to get to precision in the pharmacotherapy of depression.Toward precision medicine for depression: admitting ignorance and focusing on failures.Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting.Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.The role of GRIK4 gene in treatment-resistant depression.Developing a decision tool to identify patients with personality disorders in need of highly specialized care.Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years.Psychopathological and sociodemographic features in treatment-resistant unipolar depression versus bipolar depression: a comparative study.Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.Low hedonic tone and attention-deficit hyperactivity disorder: risk factors for treatment resistance in depressed adultsEfficacy of adjunctive low-dose cariprazine in major depressive disorder: a randomized, double-blind, placebo-controlled trial
P2860
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P2860
A clinical risk stratification tool for predicting treatment resistance in major depressive disorder
description
2013 nî lūn-bûn
@nan
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
2013年论文
@zh
2013年论文
@zh-cn
name
A clinical risk stratification ...... e in major depressive disorder
@en
type
label
A clinical risk stratification ...... e in major depressive disorder
@en
prefLabel
A clinical risk stratification ...... e in major depressive disorder
@en
P2860
P1476
A clinical risk stratification ...... e in major depressive disorder
@en
P2093
Roy H Perlis
P2860
P356
10.1016/J.BIOPSYCH.2012.12.007
P407
P577
2013-02-04T00:00:00Z