haipipe-data-3-case

To extract event-triggered features from patient time-series data, use a configuration-driven pipeline to identify trigger points and apply feature extraction functions, transforming raw records into ML-ready feature vectors.

Converting raw patient records into ML-ready feature sets involves processing RecordSets to generate CaseSets by identifying specific event triggers and applying custom feature engineering through a builder pattern.

Configure the data pipeline using a YAML configuration file to define trigger functions that identify specific events or time windows, such as CGM readings or medication events, within patient RecordSets.

Yes, you can build custom feature engineering functions for patient data transformation by defining custom CaseFn modules and utilizing the builder pattern to extract relevant features around identified trigger points.

Yes, this feature extraction approach supports generating datasets for predictive disease progression models by extracting features like glucose levels before and after events, along with demographic information, for model training.

When processing patient time-series data into structured feature vectors, limitations include dependency on accurately defined TriggerFn and CaseFn modules, requiring precise configuration to correctly identify event triggers and extract relevant features.