about
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.Predicting Consumer Effort in Finding and Paying for Health Care: Expert Interviews and Claims Data Analysis.Redesigning an Information System that Reduces Health Care Accessibility Effort and Increases User Acceptance and Satisfaction: A Comparative Effectiveness StudyAn Evaluation of Overcoming Barriers to Engage Consumers in the Use of Health Care Information TechnologyPredicting Consumer Effort in Finding and Paying for Health Care: Expert Interviews and Claims Data Analysis (Preprint)Influence of Patient Characteristics and Psychological Needs on Diabetes Mobile App Usability in Adults With Type 1 or Type 2 Diabetes: Crossover Randomized TrialMEBoost: Variable selection in the presence of measurement error
P50
Q30952713-36ADE843-A93A-4CB3-B8E4-5E6474AF44E0Q47097595-9B022163-D42A-4949-8267-E6695B388EE4Q57484486-51EC4510-D944-42AB-A42B-F82BA9C9776AQ57660590-2A34F679-75BF-4C65-9ADF-D2B13F63AC06Q57660591-80A6B3FD-E613-47EA-BC58-A3EA4CC7C738Q91636607-ED4EFF35-D0E5-42EF-9253-FC3857B87B70Q92269166-D57862EE-DF04-418D-8DBC-F8C0742D7182
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Julian Wolfson
@ast
Julian Wolfson
@en
Julian Wolfson
@es
Julian Wolfson
@nl
Julian Wolfson
@sl
type
label
Julian Wolfson
@ast
Julian Wolfson
@en
Julian Wolfson
@es
Julian Wolfson
@nl
Julian Wolfson
@sl
prefLabel
Julian Wolfson
@ast
Julian Wolfson
@en
Julian Wolfson
@es
Julian Wolfson
@nl
Julian Wolfson
@sl
P244
P101
P1153
57201661407
P21
P244
no2010127047
P31
P496
0000-0002-2032-0875