Healthcare Data Science case study
Clinical trial patient stratification and biomarker outcome analytics
An anonymised summary of the challenge, approach and outcome. Client names and measured figures are published only with written consent.
Anonymised client work
We do not publish client names or identifying details without written consent, and we do not publish numbers we cannot substantiate. The summary below describes the shape of the work without exposing the client.
Client
Healthcare Data Science sector organisation, identity withheld pending consent
Challenge
Medical research teams faced delays in identifying suitable patient cohorts and isolating therapeutic response signals from high-dimensional observational healthcare datasets.
Solution
Acmez built a secure, HIPAA/GDPR-compliant data science workbench applying unsupervised clustering, propensity score matching, and longitudinal survival models across EHR and genomic biomarkers.
Outcome
Research investigators accelerated cohort eligibility screening and isolated statistically significant patient sub-populations with audit-ready statistical validation.
Capability involved
Next step
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