Machine learning and data mining: strategies for hypothesis generation.
about
A proposal for assessing study quality: Biomonitoring, Environmental Epidemiology, and Short-lived Chemicals (BEES-C) instrumentTransitioning to a Data Driven Mental Health Practice: Collaborative Expert Sessions for Knowledge and Hypothesis FindingMultiple kernel learning with random effects for predicting longitudinal outcomes and data integration.Data Mining Techniques Applied to Hydrogen Lactose Breath TestInferring population structure and relationship using minimal independent evolutionary markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clusteringRecent developments in multivariate pattern analysis for functional MRI.A pilot study investigating changes in neural processing after mindfulness training in elite athletes.The future of medical diagnostics: large digitized databases.Distinguishing between unipolar depression and bipolar depression: current and future clinical and neuroimaging perspectives.Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.The role of machine learning in neuroimaging for drug discovery and development.Predicting suicidal behavior: are we really that far along? Comment on "Discovery and validation of blood biomarkers for suicidality".Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders.A Cross-National Tool for Assessing and Studying Suicidal Behaviors.Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus.Unsupervised classification of major depression using functional connectivity MRI.Development of diagnostic model of lung cancer based on multiple tumor markers and data mining.Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan.From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health.Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers
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P2860
Machine learning and data mining: strategies for hypothesis generation.
description
2012 nî lūn-bûn
@nan
2012 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Machine learning and data mining: strategies for hypothesis generation.
@ast
Machine learning and data mining: strategies for hypothesis generation.
@en
Machine learning and data mining: strategies for hypothesis generation.
@nl
type
label
Machine learning and data mining: strategies for hypothesis generation.
@ast
Machine learning and data mining: strategies for hypothesis generation.
@en
Machine learning and data mining: strategies for hypothesis generation.
@nl
prefLabel
Machine learning and data mining: strategies for hypothesis generation.
@ast
Machine learning and data mining: strategies for hypothesis generation.
@en
Machine learning and data mining: strategies for hypothesis generation.
@nl
P2093
P2860
P356
P1433
P1476
Machine learning and data mining: strategies for hypothesis generation.
@en
P2093
A Artés-Rodríguez
F Perez-Cruz
H Blasco-Fontecilla
H C Galfalvy
M A Oquendo
P2860
P2888
P304
P356
10.1038/MP.2011.173
P407
P577
2012-01-10T00:00:00Z
P5875
P6179
1032143563