Improve Drug Safety Through Machine Learning

Improve Drug Safety Through Machine Learning

ProQuest is committed to empowering researchers and librarians around the world. The company’s portfolio of assets — including content, technologies and deep expertise — drives better research outcomes for users and greater efficiency for the libraries and organizations that serve them.

The objective of this project is to leverage to power of machine learning to improve the literature review process. By using historical data generated by the manual review process, we will train an algorithm to identify references that contain reportable drug safety information. We will build a simple UI workflow suggesting the criteria identified by the algorithm to the literature screener.

This project requires students to develop an understanding of a typical drug safety review process used by top pharmaceutical companies. A successful project execution will result in a web UI that will apply the machine learning algorithm to a literature reference and make suggestions to the user.

More Information: 2017-ProQuest

 

Students who successfully match to this project team will be required to sign the following two documents in January 2017:

Student IP Agreement for this Project Team
Student Non-Disclosure Agreement for this Project Team
How to Apply

Project Features

  • Skill level All levels
  • Students 5-7
  • Likely Majors CE, CS, DATA, ECE, EE, IOE, MATH, MIDAS, SI, STATS
  • Course Substitutions EECS 498, CSE-G, SI, Honors, MIDAS, CE MDE, Data Science Capstone, ECE Cognate
  • IP & NDA Required? Yes
  • Summer Opportunity Interview Guaranteed
  • Machine Learning & Data Science (2-3 Students)

    Basic machine learning experience. Familiarity with algorithm creation and selection, training techniques. Python, R, Java

    • Likely Majors: EE,CSE/CS-LSA, CE, ECE, Data Science, MIDAS
  • Software Development (2-3 Students)

    Basic programming experience. Web programming experience using HTML and CSS. Web service creation and core HTTP concepts , JavaScript experience with tools like AngularJS or React

    • Likely Majors: CSE/CS-LSA
  • Human Factors Product Design (1 Student)

    Experience presenting complex data in easy to understand way. Experience designing interactions based on structured data. Usability testing. Use case modeling, requirements gathering

    • Likely Majors: Information (SI), IOE
  • Algorithm and Data Analysis (2 Students)

    • Likely Majors: MATH, STATS, Data Science, MIDAS

Primary Sponsor Mentor: John YorkJohnYork
Director of Engineering 
Over 15 years of experience as a software engineer, architect and technology leader. Industry experience in publishing, information, and software services. Worked for CareerSite.com, eePulse, JSTOR, and ProQuest.

Executive Sponsor Mentor: Roger ValadeRogerValade
VIP of Engineering 
Over 15 years of experience as a software engineer, architect and technology leader. Industry experience in publishing, information, and software services. Worked for CareerSite.com, eePulse, JSTOR, and ProQuest.

For More Information About This Sponsor, Visit Their Website (ProQuest).