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Activity Details

205 Tue, 8/4/2020, 10:00 AM - 2:00 PM Virtual
Applications of Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science, Text Analysis Interest Group
Chair(s): Jennifer Green, Montana State University
Summarizing and Extracting Insights from Consumer Review Data
Jingting Hui, PepsiCo; Jason Parcon, PepsiCo
Comparison of Machine Learning Methods with Traditional Models for Use of Public Trial Registry Data to Predict Sites Needed and Time from Study Start to Primary Completion Date
Linghui Li, AstraZeneca; Gabriela Feldberg, AstraZeneca; Faisal Khan, AstraZeneca; Sandra Smyth, AstraZeneca; Karin Schiene, AstraZeneca
Comparing Machine Learning and Penalized Regression for Predicting Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency Cohort (CRIC) Study
Jing Zhang, Moores Cancer Center, University of California, San Diego; Tobias Fuhrer, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland; Brian Kwan, University of California, San Diego; Daniel Montemayor, Department of Medicine, University of Texas Health Science Center at San Antonio; Kumar Sharma, Department of Medicine, University of Texas Health Science Center at San Antonio; Loki Natarajan, University of California, San Diego
Opportunities and Challenges in the Use of Smartphone and Smartwatch-Based Step Count Measures in Studies of Physical Activity and Health
Briana Cameron, 23andMe; Teresa Filshtein Sonmez, 23andMe; Stella Aslibekyan, 23andMe; Robert Gentleman, 23andMe
Improving Productionized Insights in Machine Learning Models Through Data-Quality Quantification
Christopher Barbour, Atrium; Paul Harmon, Atrium; Eric Loftsgaarden, Atrium