Incorporating Clinical Context into Healthcare Machine Learning

Healthcare machine learning models often rely on data extracted from clinical systems.

However, this data is heavily influenced by clinical decisions.

Ignoring this can lead to misleading results.

For example, diagnostic test frequency may correlate with severe outcomes, but this may reflect clinician concern rather than disease severity.

To address this, models should be designed with awareness of clinical workflows and decision processes.

My work focuses on applying machine learning with this perspective, integrating domain understanding with technical approaches.

I am open to remote roles globally.

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