Towards accurate and reliable ICU outcome prediction: a multimodal learning framework based on belief function theory using structured EHRs and free-text notes
Journal:
arXiv
Published Date:
Jan 8, 2025
Abstract
Accurate Intensive Care Unit (ICU) outcome prediction is critical for
improving patient treatment quality and ICU resource allocation. Existing
research mainly focuses on structured data, e.g. demographics and vital signs,
and lacks effective frameworks to integrate clinical notes from heterogeneous
electronic health records (EHRs). This study aims to explore a multimodal
framework based on belief function theory that can effectively fuse
heterogeneous structured EHRs and free-text notes for accurate and reliable ICU
outcome prediction. The fusion strategy accounts for prediction uncertainty
within each modality and conflicts between multimodal data. The experiments on
MIMIC-III dataset show that our framework provides more accurate and reliable
predictions than existing approaches. Specifically, it outperformed the best
baseline by 1.05%/1.02% in BACC, 9.74%/6.04% in F1 score, 1.28%/0.9% in AUROC,
and 6.21%/2.68% in AUPRC for predicting mortality and PLOS, respectively.
Additionally, it improved the reliability of the predictions with a 26.8%/15.1%
reduction in the Brier score and a 25.0%/13.3% reduction in negative
log-likelihood. By effectively reducing false positives, the model can aid in
better allocation of medical resources in the ICU. Furthermore, the proposed
method is very versatile and can be extended to analyzing multimodal EHRs for
other clinical tasks. The code implementation is available on
https://github.com/yuchengruan/evid_multimodal_ehr.
