Accelerate research and discovery with machine learning in HEOR and epidemiology studies

Accelerate research and discovery with machine learning in HEOR and epidemiology studies

For decades, researchers have used traditional regression methods to answer pressing questions for life sciences and healthcare research. However, with the increasing capabilities of machine learning (ML) methodologies, researchers have adopted more complex algorithms to generate compelling insights using real-world data. Proper application of machine learning methodologies can help life sciences organizations drive their research agendas forward and develop solid evidence for communication to key stakeholders such as regulatory bodies, payers, and providers.

This case study, written by IBM® Watson Health® outcomes research experts, compares five supervised learning algorithms and their considerations for use in the context of health economics and outcomes research.


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