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In recent years, many data science and natural language processing (NLP) approaches have gained popularity to derive insights from clinical narratives. Clinical NLP methods based on deep learning models have shown promising results in various information extraction tasks, such as cohort selection, medication extraction, and de-identification of patient-identifiable information. These methods and tools have also been successfully applied to facilitate clinical research, as well as to support healthcare applications.In this mini-course, I will highlight some of the challenges and potential solutions for large-scale processing of clinical narratives. I will introduce the task of clinical information extraction and present data science approaches to identify relevant information from clinical narratives. I will highlight state-of-the-art algorithms and toolkits to identify medical concepts and entities in clinical text. Based on specific examples from multiple clinical domains, I will lead a hands-on demo for developing an NLP pipeline for clinical information extraction.
The goal of this course is to provide researchers and data practitioners with practical, hands-on experience applying statistical data privacy techniques to produce shareable data sets. Many individuals working in data science applications have data they would like to share but are limited by the need to protect the privacy of the those in the data. We will cover three of the most common approaches that users may wish to implement, (1) variable suppression or recoding, (2) synthetic data, and (3) differentially private techniques. We will cover examples in which practitioners may wish to select one of these methods, give practical guidance on how to evaluate the risk and utility of the protected data, and go through example code (and available packages) for implementing the methods. At the end of the course, participants will have a basic understanding of the statistical data privacy framework, and they will have the necessary tools to start implementing multiple options for producing publicly shareable versions of data.
Are you looking for a quick way to make connections, solicit career advice, and develop professional relationships? Or maybe you want to provide advice and guidance to early-career statisticians and data scientists? Whether you are interested in mentoring or being mentored, you should consider participating in our speed mentoring session. This is a great opportunity for both mentors and mentees to build their professional networks!
Registration Forms: https://ww2.amstat.org/meetings/sdss/2022/events.cfm
