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2020 – today
- 2024
[i7]Alejandro Schuler, Alexander Hagemeister, Mark J. van der Laan:
Highly Adaptive Ridge. CoRR abs/2410.02680 (2024)- 2023
- [j3]David McCoy
, Alejandro Schuler, Alan E. Hubbard, Mark J. van der Laan:
SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R. J. Open Source Softw. 8(91): 5422 (2023) - 2022
- [j2]Catherine Lee, Brian L. Lawson, Ariana J. Mann, Vincent X. Liu, Laura C. Myers, Alejandro Schuler, Gabriel J. Escobar:
Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge. J. Am. Medical Informatics Assoc. 29(6): 1078-1090 (2022) - [i6]Alejandro Schuler, Mark J. van der Laan:
The Selectively Adaptive Lasso. CoRR abs/2205.10697 (2022) - 2021
- [c6]Ben J. Marafino, Alejandro Schuler, Vincent X. Liu, Art B. Owen, Gabriel J. Escobar, Michael Baiocchi:
Integrating Evaluations of Predictive Algorithm-Driven Interventions into Clinical Workflows with the Dynamic Discontinuity Deployment Design. AMIA 2021 - [i5]Michael O'Malley, Adam M. Sykulski, Rick Lumpkin, Alejandro Schuler:
Multivariate Probabilistic Regression with Natural Gradient Boosting. CoRR abs/2106.03823 (2021) - 2020
- [c5]Andrew Ward, Ariana J. Mann, Jacqueline Vallon, Gabriel J. Escobar, Nicholas Bambos, Alejandro Schuler:
Operationally-Informed Hospital-Wide Discharge Prediction Using Machine Learning. HealthCom 2020: 1-6 - [c4]Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler:
NGBoost: Natural Gradient Boosting for Probabilistic Prediction. ICML 2020: 2690-2700 - [i4]Alejandro Schuler, Aashish Bhardwaj, Vincent X. Liu:
Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model. CoRR abs/2006.01752 (2020) - [i3]Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher:
Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score. CoRR abs/2012.09935 (2020)
2010 – 2019
- 2019
- [i2]Tony Duan, Anand Avati, Daisy Yi Ding, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler:
NGBoost: Natural Gradient Boosting for Probabilistic Prediction. CoRR abs/1910.03225 (2019) - 2018
- [j1]Jason K. Wang, Jason Hom, Santhosh Balasubramanian, Alejandro Schuler, Nigam H. Shah, Mary K. Goldstein, Michael T. M. Baiocchi, Jonathan H. Chen:
An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. J. Biomed. Informatics 86: 109-119 (2018) - [i1]Alejandro Schuler, Nigam Shah:
General-purpose validation and model selection when estimating individual treatment effects. CoRR abs/1804.05146 (2018) - 2017
- [c3]Jason K. Wang, Alejandro Schuler, Nigam Shah, Jonathan H. Chen:
Impact of Clinician Experience on Machine Learned Clinical Order Patterns. AMIA 2017 - [c2]Alejandro Schuler, David A. Wulf, Yun Lu, Jennifer M. Baker, Gabriel J. Escobar, Nigam Shah, Vincent X. Liu:
A novel propensity modeling approach to estimate the causal impact of acute organ dysfunction on long-term survival in sepsis. CRI 2017 - 2016
- [c1]Alejandro Schuler, Vincent X. Liu, Joe Wan, Alison Callahan, Madeleine Udell, David E. Stark, Nigam H. Shah:
Discovering Patient Phenotypes Using Generalized Low Rank Models. PSB 2016: 144-155
Coauthor Index
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