Andrew Redd, PhD
- Data Interpretation, Statistical
- Clinical Natural Language Processing
- Big Data Analytics
- Data Mining in Biomedical Informatics
- Data Analysis
- Information Extraction
- Models, Statistical
- Parametric and Nonparametric Inferential Statistics
- Departments: Family and Preventive Medicine - Adjunct Assistant Professor, Internal Medicine - Research Assistant Professor
- Divisions: Epidemiology, Public Health
Academic Office Information
295 Chipeta Way
Salt Lake City, UT 84108
Andrew Redd, Ph.D., is an Assistant Professor at the Division of Epidemiology. As a Statistician, his research interests include statistical data modeling for surveillance data, disease transmission models complex data models, network models, high performance computing, statistical programming, reproducible research methods and tool, and programming and analysis standardization.Redd’s Dissertation work was on paired functional data analysis. His current research focuses on surveillance models and the statistical issues related to inference from data originating in information extraction and natural language process. He is an active contributor to the R statistical programming software through several add-on packages.Redd received his Ph.D. from Texas A&M University.
|Doctoral Training||Texas A&M University
|Graduate Training||Texas A&M University
|Internship||ATK Launch Systems
|Undergraduate||Weber State University
- Divita G, Shen S, Carter ME, Redd A, Forbush T, Palmer M, Samore MH, Gundlapalli AV (2014). Recognizing Questions and Answers in EMR Templates Using Natural Language Processing. Stud Health Technol Inform, 202, 149-52.
- Redd A, Carter M, Divita G, Shen S, Palmer M, Samore M, Gundlapalli AV (2014). Detecting earlier indicators of homelessness in the free text of medical records. Stud Health Technol Inform, 202, 153-6.
- Gundlapalli AV, Redd A, Carter ME, Palmer M, Peterson R, Samore MH (2014). Exploring patterns in resource utilization prior to the formal identification of homelessness in recently returned veterans. Stud Health Technol Inform, 202, 265-8.
- Gundlapalli A, Divita G, Carter M, Shen S, Palmer M, Forbush T, South B, Redd A, Sauer B, Samore M (3/2013). Extracting Surveillance Data from Templated Sections of an Electronic Medical Note: Challenges and Opportunities. OJPHI, 5(1).
- Gundlapalli AV, Redd A, Carter M, Divita G, Shen S, Palmer M, Samore MH (2013). Validating a strategy for psychosocial phenotyping using a large corpus of clinical text. J Am Med Inform Assoc, 20(e2), e355-64.
- Forbush TB, Gundlapalli AV, Palmer MN, Shen S, South BR, Divita G, Carter M, Redd A, Butler JM, Samore M (2013). "Sitting on pins and needles": characterization of symptom descriptions in clinical notes". AMIA Jt Summits Transl Sci Proc, 2013, 67-71.
- Gundlapalli AV, Carter ME, Palmer M, Ginter T, Redd A, Pickard S, Shen S, South B, Divita G, Duvall S, Nguyen TM, DAvolio LW, Samore M (2013). Using natural language processing on the free text of clinical documents to screen for evidence of homelessness among US veterans. AMIA Annu Symp Proc, 2013, 537-46.
- Andrew M Redd, Guy Divita, Adi V Gundlapalli, Le-Thuy T Tran, Matthew Samore (11/16/15). Novel Template Identification from VA Text Integration Utility Notes. Poster session presented at American Medical Informatics Association 21015 Annual Symposium, San Francisco,CA.