Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge, we propose Universal Healthcare Predictive Framework (UniHPF), which requires no medical domain knowledge and minimal pre-processing for multiple prediction tasks. Experimental results demonstrate that UniHPF is capable of building large-scale EHR models that can process any form of medical data from distinct EHR systems. We believe that our findings can provide helpful insights for further research on the multi-source learning of EHRs.
@article{arxiv.2211.08082,
title = {UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge},
author = {Kyunghoon Hur and Jungwoo Oh and Junu Kim and Jiyoun Kim and Min Jae Lee and Eunbyeol Cho and Seong-Eun Moon and Young-Hak Kim and Edward Choi},
journal= {arXiv preprint arXiv:2211.08082},
year = {2024}
}
Comments
The original paper is published on Journal of Biomedical and Health Informatics(JBHI) 2023, https://ieeexplore.ieee.org/document/10298642. Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States, 19 pages(main paper 6 pages). arXiv admin note: substantial text overlap with arXiv:2207.09858