Nov 19, 2021 · A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, ...

A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, ...

This last benefit allows forest-based similarities to construct meaningful relationships with observations of mixed feature types which is often a factor in ...

MURAL is a Python package for constructing random forests in an unsupervised manner from data with variables that have missing values.

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data ... data analysis, electronic medical records, random forests, unsupervised ...

Here we present the MURAL forest – an unsupervised random forest for representing data with disparate variable types (e.g., categorical, continuous, MNAR).

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. A major challenge in embedding or visualizing clinical patient data ...

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. Gerasimiuk, M., Shung, D. L., Tong, A., Stanley, A. J., Schultz, M ...

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. Michał Gerasimiuk, Dennis Shung, Alexander Y. Tong, Adrian Stanley ...

EHR. MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data · A unsupervised random tree distance for missing data in EHR.