aceImplementation of Duke Machine Learning Data Quality Assurance Framework in a Pakistani Healthcare Institution
PurposeHigh-quality data are critical for developing healthcare machine learning (ML) models yet remain a major challenge in low- and middle-income countries (LMICs). This study evaluated the applicability of the Duke Institute for Health Innovation's (DIHI) Machine Learning Data Quality Assurance (ML-DQA) framework in an LMIC setting using two ML projects at a tertiary care hospital in Pakistan.MethodsWe applied the ML-DQA framework to two ML projects, Sepsis Watch (SW) and In-hospital Mortality Prediction (IMP…
Applying a structured data quality assurance process at a Pakistani tertiary hospital identified which data elements met quality standards for sepsis and mortality prediction models, informing model development.
Evidence
- Peer-reviewedHealth Informatics Journal2026-10-07
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Truvace Impact Record TRV-2026-1769, v1: “Implementation of Duke Machine Learning Data Quality Assurance Framework in a Pakistani Healthcare Institution.” Truvace, 2026-10-18. /record/TRV-2026-1769 (accessed at citation time). sha256 3c4101739ea68cd8…
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