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Minnesota’s Premier Healthcare Data Science and Emerging Technology Conference. Presented by MinneAnalytics.
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Friday, March 29


Fighting Health Professional Burnout Through Data Analytics Im-Personalized Medicine: Exploring Treatment Gaps and Risk Stratification in Medical Devices with Healthcare Data Using Artificial Intelligence to Diagnose Diabetic Retinopathy Startup Reverse Pitch Startup Showcase Startup Showcase Alumni Panel The Importance of Diverse Data Sets in AI and ML Women In Tech Talk Emerging Tech in The Twin Cities Solving the Funding Impedimnet (Agile Funding) Artificial Intelligence Overview – What Does it all Mean? Using AI to Optimize Healthcare Operations Utilizing PowerBI for Self Service Analytics A Roadmap for Analytics (R)Evolution Adventures in Cinical Data-mining: Mining for Gold or Dumpster Diving? AI Powered ECG Interpretation Automating Data Aggregation, Ingestion and Analysis from Multiple Health Sources Biometrics, Machine Learning and Mental Health Blood Glucose Prediction and Decision Support for People with Type 2 Diabetes Creating, Branding, Protecting and Monetizing AI Assets Data as the Foundation of Life Science Innovation Healthcare Billing Analytics: Transform Big Data into Revenue Healthcare Needs an Enterprise Strategy and Data Layer to Make Data an Enterprise Asset Impacting Chronic Care with AI and IoT Leveraging insights about the relationship between Health and Human Services Machine Learning Models in Production Driving Insight into OptumRx’s Programs Making Artificial Intelligence Clinically Actionable: Individualizing Predictions of Antidepressant Response Personalized Diabetes Management, Building Blocks with Optum Data Assets Predictive Clinical Surveillance with AI Transforming Healthcare with Real-time Patient and Prescriber 360 Built on a Native Parallel Graph Using Graph Theory for Narcotics Dependence/Abuse Identification Using Patient Reported Outcomes Data Insights Effectively to Deliver Value to Payers and Providers Vital Information Standardization: An Artificial Intelligence Approach Advanced PySpark ML Applying Objective Data and Context to Manage Human Health Behaviors Experimenting with Neural Networks and Graph Databases to Assist in Semantic Alignment of Clinical Outcomes Data of Stem Cell Transplantation within the NMDP Improve your Models with Causal Feature Selection Machine Learning in Telehealth Medical Reversals: Understanding De-adoption of Ineffective or Unsafe Treatments Overcoming the Challenges of Software 2.0 for Healthcare Predictive Analytics and Alzheimer's Disease Quality Matters: Understanding Healthcare Data Quality and Fitness-for-Use Real World Success Stories of Artificial Intelligence in Medicine: A case study in Neurology - Spatial & Temporal Epileptic Seizure Forecasting SleepWell: Analytics Toolkit for Sleep Health and Wellness Topology of food intake and health outcomes Containerize Your Data Science Work Environment: Why & How Deep Learning Approaches in Health Care