Key points:

• Predictive Models: Forecast future outcomes based on historical data.
• Model Building: Process of analyzing data, training, and testing models.
• Validation: Ensures model reliability and accuracy.
• Healthcare Applications: Use in survival rates, treatment responses.
• Technical Discussion: Pruning models, interpretability, data considerations.
• Tools: Use of R, Python, SAS for building predictive models.
• Collaboration: Importance of interdisciplinary work, combining traditional statistics with AI/ML techniques.

Chantelle shares valuable insights into building, testing, and validating these models, showing just how powerful and essential they are.

If you found this discussion insightful, listen to the full episode to dive deeper into the technical details and practical applications of predictive modeling. And if you know others who would benefit from this knowledge, share the episode with them. Let’s spread these valuable insights together!