3 citations · 8 across the 7 of their papers we have counts for
7 papers
High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records
Alex Taylor, Ross Kleiman, Scott Hebbring +2
Accurate estimation of healthcare costs is crucial for healthcare systems to plan and effectively negotiate with insurance companies regarding the coverage of patient-care costs. G…
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…
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…
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…
Computational Drug Repositioning Using Continuous Self-controlled Case Series
Zhaobin Kuang, James Thomson, Michael Caldwell +3
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Levera…
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…
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…