The Translation Problem in Healthcare ML Systems

Healthcare ML systems often fail due to misalignment between domains.

The Problem

Data scientists build models.
Clinicians make decisions.

But the connection between the two is weak.

Where It Breaks
Features lack clinical meaning
Outputs are not actionable
Results are poorly communicated
What Translation Looks Like
Mapping model outputs to clinical decisions
Interpreting features in domain context
Communicating results clearly to stakeholders
Practical Approach
collaborate with domain experts
validate assumptions with clinical input
design outputs for usability
Key Insight

In healthcare ML, success depends on connecting domains—not just building models.

I am open to remote roles globally.