Work

Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping

Year: 2020

Type: article

Abstract: , we propose a prior adaptive semi-supervised (PASS) estimator that incorporates the prior knowledge by shrinking the estimator towards a direction derived under the prior. We derive asymptotic theory for the proposed estimator and justify its efficiency and robustness to prior information of poor quality. We also demonstrate its superiority over existing estimators under various scenarios via simulation studies and on three real-world EHR phenotyping studies at a large tertiary hospital.

Source: PubMed

DOI: 10.48550/arxiv.2003.11744

Authors: Zhang, Yichi, Liu, Molei, Neykov, Matey, Cai, Tianxi

Institutions: University of Rhode Island, Harvard University, Carnegie Mellon University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, U.S. National Library of Medicine

Awards: Statistical Learning for Precision Medicine Based on Multi-Source Data, G977477354