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Content
Welcome and Introduction
Artificial Intelligence Applications in Kidney Diseases
Barriers to and Methods of Promoting Evidence-Based Conservative Kidney Management
Bringing Machine Learning Models to the Bedside
Combining Artificial and Collective Intelligence in Evidence Synthesis
Data Mining the Electronic Health Record
From Jargon to Intuition
Implementation of Precision Medicine in Diabetic Kidney Diseases
Implementation Strategies Using the Electronic Health Record
Implementing a New Strategy to Solve an Old Problem
Incorporating Implementation Strategies into Hemodialysis Practice
Integrating Machine Learning Models into Clinical Practice
Intervention Implementation in Pragmatic Randomized Trials
Kidney Biopsy Specimens
Lessons Learned from the AKI Alerts Trial
Machine Learning Opportunities and Challenges
Machine Learning vs. Other Stats
The Big (Data) Picture
Training and Validation of AKI Models for Clinical Practice
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