activity
20122020
most citedDemand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events

3 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20201 cited

Temporal Poisson Square Root Graphical Models

Sinong Geng, Zhaobin Kuang, Peggy Peissig +1

We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal…

q-bio.QM20191 cited

High-Throughput Machine Learning from Electronic Health Records

Ross S. Kleiman, Paul S. Bennett, Peggy L. Peissig +5

The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disea…

cs.GR20192 cited

An Application of Manifold Learning in Global Shape Descriptors

Fereshteh S. Bashiri, Reihaneh Rostami, Peggy Peissig +2

With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to p…

stat.ME2012

Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies

Jie Liu, Chunming Zhang, Catherine McCarty +3

Large-scale multiple testing tasks often exhibit dependence, and leveraging the dependence between individual tests is still one challenging and important problem in statistics. Wi…

cs.LG20123 cited

Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events

Jesse Davis, Vitor Santos Costa, Peggy Peissig +3

Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statist…