Search Results for author: Chirag Nagpal

Found 8 papers, 5 papers with code

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

2 code implementations15 Apr 2022 Chirag Nagpal, Willa Potosnak, Artur Dubrawski

Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death.

Time-to-Event Prediction

Counterfactual Phenotyping with Censored Time-to-Events

2 code implementations22 Feb 2022 Chirag Nagpal, Mononito Goswami, Keith Dufendach, Artur Dubrawski

Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring.

Deep Cox Mixtures for Survival Regression

4 code implementations16 Jan 2021 Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh, Katherine Heller

Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up.

Survival Analysis

Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise

no code implementations14 Apr 2020 Chirag Nagpal, Robert E. Tillman, Prashant Reddy, Manuela Veloso

We consider the problem of aggregating predictions or measurements from a set of human forecasters, models, sensors or other instruments which may be subject to bias or miscalibration and random heteroscedastic noise.

Bayesian Inference

Nonlinear Semi-Parametric Models for Survival Analysis

1 code implementation14 May 2019 Chirag Nagpal, Rohan Sangave, Amit Chahar, Parth Shah, Artur Dubrawski, Bhiksha Raj

Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis.

Survival Analysis

Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models

no code implementations23 Jun 2017 Abhilasha Ravichander, Shruti Rijhwani, Rajat Kulshreshtha, Chirag Nagpal, Tadas Baltrušaitis, Louis-Philippe Morency

In this work, we focus on improving learning for such hierarchical models and demonstrate our method on the task of speaker trait prediction.

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