Published Jun 6, 2022

Haywire Algorithms

Ravi Parikh delves into the impact of the pandemic on machine learning models in clinical care, discussing operational challenges like model drift, the need for retraining and clinician involvement, and the critical role of real-time monitoring to ensure reliability and effectiveness in patient outcomes.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Kyle Polich

Haywire Algorithms

Topics covered

Popular Clips

Human in the Loop

Model Review Process

Pandemic Impact on Models

Model Drift Awareness

Mortality Prediction Tool

Data Insights

Medical Feature Susceptibility

Machine Learning in Healthcare

Machine Learning Retraining

Machine Learning in Healthcare

Clinical Outcome Predictions

Enhancing Model Robustness

Episode Highlights

Operational Challenges

Machine Learning Development

Pandemic Model Challenges

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Drug Discovery with Machine Learning

Pandemic Machine Learning Pitfalls

Machine Learning Done Wrong

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ML Ops

Haywire Algorithms

Topics covered

Popular Clips

Episode Highlights

Operational ChallengesIn "Haywire Algorithms," Ravi Parikh explores the impact of the pandemic on machine learning models in clinical care. He highlights the challenges of model drift and the importance of real-time monitoring to maintain model reliability.

Operational Challenges

Machine Learning Development

Pandemic Model ChallengesRavi Parikh explores how the pandemic-induced shifts in healthcare utilization affected machine learning models, particularly in clinical settings. He discusses the challenges of feature drift and the importance of regular model reviews to maintain accuracy and reliability.

Pandemic Model Challenges

Related Episodes