Jan 13, 2020 · We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous ...

This work extended the state-of-the-art by considering variable patient history lengths before the occurrence of an ADE event rather than a patient history ...

Jan 24, 2020 · We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous ...

Traditional machine learning algorithms are used to predict adverse drug events as a classification problem. In 11 out of 82 included publications (around ...

We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous data types ...

“Mining Adverse Drug Events Using Multiple Feature Hierarchies and Patient History Windows.” 2019 International Conference on Data Mining Workshops (ICDMW).

However, mining EHRs for potential ADEs, which typically involves identification of statistical associations between drugs and medical conditions, introduced ...

The FDA uses a data mining engine to compute signal scores (statistical reporting associations) for all of the millions of drug-event combinations in AERS, ...

Abstract [en]. We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous data ...

May 16, 2020 · Abstract. Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs).