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pro vyhledávání: '"Chang, Joshua C"'
We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models. One can leverage th
Externí odkaz:
http://arxiv.org/abs/2402.08151
Autor:
Xia, Hongjing, Chang, Joshua C., Nowak, Sarah, Mahajan, Sonya, Mahajan, Rohit, Chang, Ted L., Chow, Carson C.
Publikováno v:
PMLR 219:884-905, 2023
We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an i
Externí odkaz:
http://arxiv.org/abs/2304.09981
Publikováno v:
Western Journal of Emergency Medicine, Vol 10, Iss 1, Pp 42-47 (2009)
Ambulance response times in Santa Barbara County for 2006 are analyzed using point process techniques, including kernel intensity estimates and K-functions. Clusters of calls result in significantly higher response times, and this effect is quantifie
Externí odkaz:
https://doaj.org/article/14d4e24861b044c6b03e33b3cc14ba00
Publikováno v:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research 206:3961-3976, 2023
The Work Disability Functional Assessment Battery (WD-FAB) is a multidimensional item response theory (IRT) instrument designed for assessing work-related mental and physical function based on responses to an item bank. In prior iterations it was dev
Externí odkaz:
http://arxiv.org/abs/2210.10952
Autor:
Chang, Joshua C., Chang, Ted L., Chow, Carson C., Mahajan, Rohit, Mahajan, Sonya, Maisog, Joe, Vattikuti, Shashaank, Xia, Hongjing
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival mod
Externí odkaz:
http://arxiv.org/abs/2208.12814
Autor:
Chang, Joshua C., Ryan, Molly R., Stark, Marie C., Liu, Su, Purushothaman, Pravinkumar, Bolan, Fria, Johnson, Caitlin A., Champe, Mark, Meng, Hui, Lawlor, Michael W., Halawani, Sarah, Ngaba, Lucie V., Lynch, David R., Davis, Crystal, Gonzalo-Gil, Elena, Lutz, Cathleen, Urbinati, Fabrizia, Medicherla, Bala, Fonck, Carlos
Publikováno v:
In Molecular Therapy - Methods & Clinical Development 14 March 2024 32(1)
Autor:
Chang, Joshua C., Fletcher, Patrick, Han, Jungmin, Chang, Ted L., Vattikuti, Shashaank, Desmet, Bart, Zirikly, Ayah, Chow, Carson C.
Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other s
Externí odkaz:
http://arxiv.org/abs/2012.04171
Publikováno v:
PLoS ONE 17(4): e0266350 (2022)
Item response theory (IRT) is the statistical paradigm underlying a dominant family of generative probabilistic models for test responses, used to quantify traits in individuals relative to target populations. The graded response model (GRM) is a par
Externí odkaz:
http://arxiv.org/abs/2010.01396
Item response theory (IRT) is a non-linear generative probabilistic paradigm for using exams to identify, quantify, and compare latent traits of individuals, relative to their peers, within a population of interest. In pre-existing multidimensional I
Externí odkaz:
http://arxiv.org/abs/1912.02351
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