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.
hub
Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research
2 Pith papers cite this work, alongside 1,197 external citations. Polarity classification is still indexing.
2
Pith papers citing it
1,197
external citations · OpenAlex
hub tools
fields
cs.LG 2representative citing papers
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.
citing papers explorer
-
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.
-
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.