Using machine learning to improve anaphylaxis case identification in medical claims data.

Objectives: Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

Materials And Methods: This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets.

Using machine learning to improve anaphylaxis case identification in medical claims data.

Objective: Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

Methods: This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets.

Communicating regulatory high-throughput sequencing data using BioCompute Objects.

This project demonstrates the use of the IEEE 2791-2020 Standard (BioCompute Objects [BCO]) to enable the complete and concise communication of results from next generation sequencing (NGS) analysis. One arm of a clinical trial was replicated using synthetically generated data made to resemble real biological data and then two independent analyses were performed. The first simulated a pharmaceutical regulatory submission to the US Food and Drug Administration (FDA) including analysis of results and a BCO.

Use of a rapid electronic survey methodology to estimate blood donors' potential exposure to emerging infectious diseases: Application of a statistically representative sampling methodology to assess risk in US blood centers.

Unlabelled: Risk assessments of transfusion-transmitted emerging infectious diseases (EIDs) are complicated by the fact that blood donors' demographics and behaviors can be different from the general population. Therefore, when assessing potential blood donor exposure to EIDs, the use of general population characteristics, such as U.S.