It is estimated that approximately 80% of data in most organizations is unstructured, such as text. This webinar will provide an overview of new methods easily implemented to find previously unknown relationships from a collection of unstructured data. Techniques that are used to explore text from various sources (such as survey comments, incident reports, free form data fields, websites, research reports, and social media)will be demonstrated. These methods will show how to discover potentially useful and actionable compliance and business insights. Multiple demonstrations with example datasets that include applications to incident reports, survey results, FDA recalls, inspection observations, and other meaningful case studies applicable to excellence in compliance and business.
Most pharmaceutical, biopharmaceutical, and medical device organizations are analyzing structured numerical (and categorical)
However, the majority of stored data is not numerical; it is in the form of unstructured text in reports and documents. How do you analyze product complaints to see if there are systemic themes? How do these systemic themes relate to minor, major, and severe outcomes? How do you conduct analysis on the free-form sections of non-conformances?
New text analysis techniques can be easily implemented to find previously unknown relationships from these collections of unstructured data. These methods candiscover useful and actionable compliance and business insights.
Why Should you Attend:
Most companies are spending resources to collect unstructured text data but are not doing anything with it. In this webinar, participants will be guided through end-to-end examples starting from assembling disparate text sources, followed by creating a structured dataset, then applying data mining methods such as decision trees, regression, and cluster analysis to discover useful relationships. While relevant theory will be discussed, the focus of the course will be on giving participants an appreciation for the practical application of text mining to real-world applications in FDA-regulated companies.
Objectives of the Presentation:
Introduction to Text Mining
Application Example: medical device recalls
Overview of Data Mining
Natural Language Processing
Application Examples: accident reports, surveys, information from website crawling and social media, and inspection observations.
Who can Benefit:
This webinaris designed for pharmaceutical, biopharmaceutical, and medical device professionals who are involved with the analysis of data for making compliance and business decisions.
Continuous Improvement Professional
Toll free: +1-510-857-5896
38868 Salmon Ter,
Fremont, CA 94536, USA
Nihar Ranjan Mohanty
Nihar Ranjan Mohanty