New Fellowship in drug-drug interactions in large and disparate healthcare datasets

December 2019

We are pleased to announce that we are recruiting for a new postdoctoral fellow in the CRS Regulatory Science Fellowship Program. This fellow will work with a multidisciplinary team to develop and evaluate novel computational methods supporting automated detection of drug-drug interactions in large and disparate healthcare datasets. This position will be based with the larger cohort of regulatory science fellows at the Harvard Program in Therapeutic Science as well as at the Avillach lab in DBMI.
 
We invite interested individuals to apply by submitting their materials to Dr. Paul Avillach. Learn more about this position here, or read more about the Harvard-MIT CRS Regulatory Science Fellowship here.

Project Details

The application of healthcare claims data and electronic health records for use in pharmacoepidemiological studies is booming. Drug–drug interactions can be identified by mining such databases, including adverse drug reactions (ADRs) resulting from the prescription of a combination of drugs (CADRs), considered a special class of drug-drug interactions. While many ADRs can be anticipated and hence avoided, CADRs are highly complex and challenging to identify and, as a consequence, have not been well studied. CADRs may represent as many as 30 % of unexpected ADRs and their elucidation is of particular interest to the FDA. The fellowship project will develop new signal detection algorithms using data mining techniques in real-world healthcare data derived from claims and electronic healthcare records. The project will aim to identify potential pharmacovigilance signals concerning drug-drug interactions across multiple diseases and syndromes.

Research activities include, but are not limited to:

• Extract and analyze information from a nationwide claims database including 67 million subjects
• Extract and analyze information from healthcare records in an academic pediatric hospital including 1.8 million patients
• Develop and test methods to detect adverse drug reactions from billing codes, drug prescriptions and lab value results
• Disseminate findings through national and international conferences and in peer-reviewed scientific publications

Basic Qualifications

• PhD in computer science, engineering or a related field
• Expertise in SQL query language
• Expertise in R or Python programing languages

Application Procedure and Requirements 

Applications will be accepted until the position is filled. Please send your application to to paul_avillach@hms.harvard.edu and include the following:

• Curriculum vitae
• Cover letter and a 2-page description of relevant experience as a single PDF
• DOI or PMCID of up to three relevant publications

Selected candidates will be asked to provide letters of reference.