Search Results for author:
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
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.
Counterfactual Phenotyping with Censored Time-to-Events
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
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.
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise
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.
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner.
Nonlinear Semi-Parametric Models for Survival Analysis
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis.
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States.
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
In this work, we focus on improving learning for such hierarchical models and demonstrate our method on the task of speaker trait prediction.
