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A key challenge we face at Pacmed is quickly calibrating and deploying our tools for clinical decision support in different hospitals, where data formats may vary greatly. Using Intensive Care Units as a case study, I’ll delve into our scalable Python pipeline, which leverages Pandas’ split-apply-combine approach to perform complex feature engineering and automatic quality checks on large time-varying data, e.g. vital signs. I’ll show how we use the resulting flexible and interpretable dataframes to quickly (re)train our models to predict mortality, discharge, and medical complications.