Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Abstract
The automatic development of phenotype algorithms from Electronic Health Record data with machine learning (ML) techniques is of great interest given the current practice is very time-consuming and resource intensive. The extraction of design patterns from phenotype algorithms is essential to understand their rationale and standard, with great potential to automate the development process. In this pilot study, we perform network visualization on the design patterns and their associations with phenotypes and sites. We classify design patterns using the fragments from previously annotated phenotype algorithms as the ground truth. The classification performance is used as a proxy for coherence at the attribution level. The bag-of-words representation with knowledge-based features generated a good performance in the classification task (0.79 macro-f1 scores). Good classification accuracy with simple features demonstrated the attribution coherence and the feasibility of automatic identification of design patterns. Our results point to both the feasibility and challenges of automatic identification of phenotyping design patterns, which would power the automatic development of phenotype algorithms.
Cite
@article{arxiv.1811.06183,
title = {Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms},
author = {Yizhen Zhong and Luke Rasmussen and Yu Deng and Jennifer Pacheco and Maureen Smith and Justin Starren and Wei-Qi Wei and Peter Speltz and Joshua Denny and Nephi Walton and George Hripcsak and Christopher G Chute and Yuan Luo},
journal= {arXiv preprint arXiv:1811.06183},
year = {2018}
}
Comments
4 pages, accepted by IEEE BIBM 2018 as short paper