Introduces dashi, a Python library with unsupervised (information geometry-based) and supervised methods to quantify temporal and multi-source dataset shifts for trustworthy health AI.
Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research
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SurvBench supplies a configurable, open-source preprocessing pipeline that standardizes multi-modal EHR data from four critical-care databases for single-risk and competing-risk survival analysis.
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dashi: A Python library for Dataset Shift Characterization to Support Trustworthy AI Development and Deployment
Introduces dashi, a Python library with unsupervised (information geometry-based) and supervised methods to quantify temporal and multi-source dataset shifts for trustworthy health AI.
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SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis
SurvBench supplies a configurable, open-source preprocessing pipeline that standardizes multi-modal EHR data from four critical-care databases for single-risk and competing-risk survival analysis.