Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning

no code implementations • 3 May 2023 • Shenbo Xu, Bang Zheng, Bowen Su, Stan Finkelstein, Roy Welsch, Kenney Ng, Ioanna Tzoulaki, Zach Shahn

Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification.

Causal Inference

Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge

no code implementations • 9 Apr 2021 • Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin Walsh, Jonathan Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley A. Malin

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.

BIG-bench Machine Learning

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes

no code implementations • 23 Mar 2020 • Rui Li, Zach Shahn, Jun Li, Mingyu Lu, Prithwish Chakraborty, Daby Sow, Mohamed Ghalwash, Li-wei H. Lehman

Counterfactual prediction is a fundamental task in decision-making.

Decision Making regression +2

ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPORAL EMBEDDING

no code implementations • 27 Sep 2018 • Daby Sow, Mohamed Ghalwash, Zach Shahn, Sanjoy Dey, Moulay Draidia, Li-wei Lehmann

Learning explainable patient temporal embeddings from observational data has mostly ignored the use of RNN architecture that excel in capturing temporal data dependencies but at the expense of explainability.