Yale Chang
Yale Chang
Philips Research North America
Verified email at - Homepage
Cited by
Cited by
Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories.
EH Siegel, MK Sands, W Van den Noortgate, P Condon, Y Chang, J Dy, ...
Psychological bulletin 144 (4), 343, 2018
Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema
PJ Castaldi, J Dy, J Ross, Y Chang, GR Washko, D Curran-Everett, ...
Thorax 69 (5), 416-423, 2014
A Wide & Deep Transformer Neural Network for 12-Lead ECG Classification
A Natarajan, Y Chang, S Mariani, A Rahman, G Boverman, S Vij, J Rubin
International Conference in Computing in Cardiology, 2020
COPD subtypes identified by network-based clustering of blood gene expression
Y Chang, K Glass, YY Liu, EK Silverman, JD Crapo, R Tal-Singer, ...
Genomics 107 (2-3), 51-58, 2016
Lobar emphysema distribution is associated with 5-year radiological disease progression
A Boueiz, Y Chang, MH Cho, GR Washko, RSJ Estépar, RP Bowler, ...
Chest 153 (1), 65-76, 2018
Interpretable clustering via discriminative rectangle mixture model
J Chen, Y Chang, B Hobbs, P Castaldi, M Cho, E Silverman, J Dy
2016 IEEE 16th international conference on data mining (ICDM), 823-828, 2016
Phenotypic and genetic heterogeneity among subjects with mild airflow obstruction in COPDGene
JH Lee, MH Cho, MLN McDonald, CP Hersh, PJ Castaldi, JD Crapo, ...
Respiratory medicine 108 (10), 1469-1480, 2014
Informative subspace learning for counterfactual inference
Y Chang, J Dy
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
A Robust-Equitable Copula Dependence Measure for Feature Selection
Y Chang, Y Li, A Ding, J Dy
The 19th International Conference on Artificial Intelligence and Statistics …, 2016
A Bayesian nonparametric model for disease subtyping: application to emphysema phenotypes
JC Ross, PJ Castaldi, MH Cho, J Chen, Y Chang, JG Dy, EK Silverman, ...
IEEE transactions on medical imaging 36 (1), 343-354, 2016
Early prediction of hemodynamic interventions in the intensive care unit using machine learning
A Rahman, Y Chang, J Dong, B Conroy, A Natarajan, T Kinoshita, ...
Critical Care 25, 1-9, 2021
Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome
E Schwager, K Jansson, A Rahman, S Schiffer, Y Chang, G Boverman, ...
NPJ Digital Medicine 4 (1), 133, 2021
A robust-equitable measure for feature ranking and selection
AA Ding, JG Dy, Y Li, Y Chang
Journal of Machine Learning Research 18 (71), 1-46, 2017
Multiple clustering views from multiple uncertain experts
Y Chang, J Chen, MH Cho, PJ Castaldi, EK Silverman, JG Dy
International Conference on Machine Learning, 674-683, 2017
Clustering with domain-specific usefulness scores
Y Chang, J Chen, MH Cho, PJ Castaidi, EK Silverman, JG Dy
Proceedings of the 2017 SIAM International Conference on Data Mining, 207-215, 2017
Solving interpretable kernel dimension reduction
C Wu, J Miller, Y Chang, M Sznaier, J Dy
arXiv preprint arXiv:1909.03093, 2019
A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series
Y Chang, J Rubin, G Boverman, S Vij, A Rahman, A Natarajan, ...
International Conference in Computing in Cardiology 46, 2019
Solving interpretable kernel dimensionality reduction
C Wu, J Miller, Y Chang, M Sznaier, J Dy
Advances in Neural Information Processing Systems 32, 2019
Convolution-free waveform transformers for multi-lead ECG classification
A Natarajan, G Boverman, Y Chang, C Antonescu, J Rubin
2021 Computing in Cardiology (CinC) 48, 1-4, 2021
Early Prediction of Cardiogenic Shock Using Machine Learning
Y Chang, C Antonescu, S Ravindranath, J Dong, M Lu, F Vicario, ...
Frontiers in Cardiovascular Medicine, 1868, 2022
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