Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.
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A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study.Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.Realising stratified psychiatry using multidimensional signatures and trajectories.Prevention of Mental Health Disorders Using Internet- and Mobile-Based Interventions: A Narrative Review and Recommendations for Future Research.Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study.[Big data approaches in psychiatry: examples in depression research].Development of New Diagnostic Techniques - Machine Learning.A Preliminary Study of Genetic Variation in the Dopaminergic and Serotonergic Systems and Genome-wide Additive Genetic Effects on Depression Severity and Treatment Response.Cognitive and Interpersonal Vulnerabilities to Adolescent Depression: Classification of Risk Profiles for a Personalized Prevention Approach.Problematic internet use as an age-related multifaceted problem: Evidence from a two-site survey.Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain.Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.Efficacy and moderators of psychological interventions in treating subclinical symptoms of depression and preventing major depressive disorder onsets: protocol for an individual patient data meta-analysis of randomised controlled trials.Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approachPharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining
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P2860
Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.
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article científic
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article scientifique
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articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on 05 January 2016
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Testing a machine-learning alg ...... er from baseline self-reports.
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Testing a machine-learning alg ...... er from baseline self-reports.
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Testing a machine-learning alg ...... er from baseline self-reports.
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Testing a machine-learning alg ...... er from baseline self-reports.
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Testing a machine-learning alg ...... er from baseline self-reports.
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Testing a machine-learning alg ...... er from baseline self-reports.
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Testing a machine-learning alg ...... der from baseline self-reports
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A A Nierenberg
A J Rosellini
A M Zaslavsky
H M van Loo
K J Wardenaar
L A Brenner
M A Wilcox
M V Petukhova
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10.1038/MP.2015.198
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2016-01-05T00:00:00Z