Alejandro Schuler

According to our database, Alejandro Schuler authored at least 15 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R.

David McCoy, Alejandro Schuler, Alan E. Hubbard, Mark J. van der Laan

J. Open Source Softw., November, 2023

2022

Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge.

Catherine Lee, Brian L. Lawson, Ariana J. Mann, Vincent X. Liu, Laura C. Myers, Alejandro Schuler, Gabriel J. Escobar

J. Am. Medical Informatics Assoc., 2022

The Selectively Adaptive Lasso.

Alejandro Schuler, Mark J. van der Laan

CoRR, 2022

2021

Multivariate Probabilistic Regression with Natural Gradient Boosting.

Michael O'Malley, Adam M. Sykulski, Rick Lumpkin, Alejandro Schuler

CoRR, 2021

Integrating Evaluations of Predictive Algorithm-Driven Interventions into Clinical Workflows with the Dynamic Discontinuity Deployment Design.

Ben J. Marafino, Alejandro Schuler, Vincent X. Liu, Art B. Owen, Gabriel J. Escobar, Michael Baiocchi

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score.

Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher

CoRR, 2020

Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model.

Alejandro Schuler, Aashish Bhardwaj, Vincent X. Liu

CoRR, 2020

NGBoost: Natural Gradient Boosting for Probabilistic Prediction.

Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler

Proceedings of the 37th International Conference on Machine Learning, 2020

Operationally-Informed Hospital-Wide Discharge Prediction Using Machine Learning.

Andrew Ward, Ariana J. Mann, Jacqueline Vallon, Gabriel J. Escobar, Nicholas Bambos, Alejandro Schuler

Proceedings of the 22nd IEEE International Conference on E-health Networking, 2020

2019

NGBoost: Natural Gradient Boosting for Probabilistic Prediction.

Tony Duan, Anand Avati, Daisy Yi Ding, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler

CoRR, 2019

2018

An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.

Jason K. Wang, Jason Hom, Santhosh Balasubramanian, Alejandro Schuler, Nigam H. Shah, Mary K. Goldstein, Michael T. M. Baiocchi, Jonathan H. Chen

J. Biomed. Informatics, 2018

General-purpose validation and model selection when estimating individual treatment effects.

Alejandro Schuler, Nigam Shah

CoRR, 2018

2017

A novel propensity modeling approach to estimate the causal impact of acute organ dysfunction on long-term survival in sepsis.

Alejandro Schuler, David A. Wulf, Yun Lu, Jennifer M. Baker, Gabriel J. Escobar, Nigam Shah, Vincent X. Liu

Proceedings of the Summit on Clinical Research Informatics, 2017

Impact of Clinician Experience on Machine Learned Clinical Order Patterns.

Jason K. Wang, Alejandro Schuler, Nigam Shah, Jonathan H. Chen

Proceedings of the AMIA 2017, 2017

2016

Discovering Patient Phenotypes Using Generalized Low Rank Models.

Alejandro Schuler, Vincent X. Liu, Joe Wan, Alison Callahan, Madeleine Udell, David E. Stark, Nigam H. Shah

Proceedings of the Biocomputing 2016: Proceedings of the Pacific Symposium, 2016