A Precision Oncology Platform to Support Genotype-Tailored Therapy Selection.

There is a critical need for a streamlined process to identify genotype-matched individuals eligible for enrollment into clinical trials and/or targeted therapies, as current methodologies face challenges in integrating diverse molecular data sources. We have developed a precision oncology platform to assist molecular tumor boards and community oncologists in reviewing patients' phenotypes, evaluating related knowledge, and identifying genotype-matched therapies.

Overcoming Major Barriers to Build Efficient Decision Support Systems in Pharmacovigilance.

Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their eventual success. We describe two sets of challenges and appropriate strategies to address them. The first are data-related challenges, which include using extensive multi-source data of poor quality, incomplete information integration, and inefficient data visualization.

Feature engineering and machine learning for causality assessment in pharmacovigilance: Lessons learned from application to the FDA Adverse Event Reporting System.

Background: Our objective was to support the automated classification of Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports for their usefulness in assessing the possibility of a causal relationship between a drug product and an adverse event.

Method: We used a data set of 326 redacted FAERS reports that was previously annotated using a modified version of the World Health Organization-Uppsala Monitoring Centre criteria for drug causality assessment by a group of SEs at the FDA and supported a similar study on the classification of reports using supervised machine learning and text engineering methods. We explored many potential features, including the incorporation of natural language processing on report text and information from external data sources, for supervised learning and developed models for predicting the classification status of reports.

Information Visualization Platform for Postmarket Surveillance Decision Support.

Introduction: The US FDA receives more than 2 million postmarket reports each year. Safety Evaluators (SEs) review these reports, as well as external information, to identify potential safety signals. With the increasing number of reports and the size of external information, more efficient solutions for data integration and decision making are needed.