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Activity Number: 646 - Applications of Deep Learning in Pharmaceutical Development
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #305116 Presentation
Title: Leveraging Free Text Data for Decision Making in Drug Development
Author(s): Yan Sun* and Jiyeong Jang and Xin Huang and Hongwei Wang and Weili He
Companies: AbbVie and University of Illinois at Chicago and AbbVie Inc. and AbbVie Inc. and AbbVie
Keywords: natural language processing; electronic health records; deep learning

With the wide adoption of electronic health records and popularity of social media and digital device, rich information for population at large is being accumulated on a daily basis. The information in many cases take the form of free texts that includes physician notes, on-line posting, and readings from device that could greatly augment the structured data to facilitate evidence-based data generation and decision-making. At the same time, unstructured data also poses challenges for analyses as classical statistical models are mostly developed for structured data. In this talk, we will present an end to end process that is required for a natural language processing research project. This includes pre-processing, e.g., tokenization, filtering, stemming, and lemmatization, followed by two different frameworks. The first one is to transform free text into structured data after feature extraction that can be fed into appropriate machine learning algorithm for model derivation. The second one is to leverage the most recent development in deep learning models which has feature extraction embedded. Practical consideration of their application, tools and case studies will be given.

Authors who are presenting talks have a * after their name.

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