3 citations · 4 across the 3 of their papers we have counts for
3 papers
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…
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…
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…