Document Type
Article
Publication Date
10-7-2024
Abstract
Ensuring accurate predictions of inpatient length of stay (LoS) and mortality rates is essential for enhancing hospital service efficiency, particularly in light of the constraints posed by limited healthcare resources. Integrative analysis of heterogeneous clinic record data from different sources can hold great promise for improving the prognosis and diagnosis level of LoS and mortality. Currently, most existing studies solely focus on single data modality or tend to single-task learning, i.e., training LoS and mortality tasks separately. This limits the utilization of available multi-modal data and prevents the sharing of feature representations that could capture correlations between different tasks, ultimately hindering the model’s performance. To address the challenge, this study proposes a novel Multi-Modal Multi-Task learning model, termed as M3T-LM, to integrate clinic records to predict inpatients’ LoS and mortality simultaneously. The M3T-LM framework incorporates multiple data modalities by constructing sub-models tailored to each modality. Specifically, a novel attention-embedded one-dimensional (1D) convolutional neural network (CNN) is designed to handle numerical data. For clinical notes, they are converted into sequence data, and then two long short-term memory (LSTM) networks are exploited to model on textual sequence data. A two-dimensional (2D) CNN architecture, noted as CRXMDL, is designed to extract high-level features from chest X-ray (CXR) images. Subsequently, multiple sub-models are integrated to formulate the M3T-LM to capture the correlations between patient LoS and modality prediction tasks. The efficiency of the proposed method is validated on the MIMIC-IV dataset. The proposed method attained a test of 5.54 for LoS prediction and a test 1 of 0.876 for mortality prediction. The experimental results demonstrate that our approach outperforms state-of-the-art (SOTA) methods in tackling mixed regression and classification tasks.
Recommended Citation
J. Chen, Q. Li, F. Liu, and Y. Wen, "M3T-LM: A multi-modal multi-task learning model for jointly predicting patient length of stay and mortality," Computers in Biology and Medicine, vol. 183, 109237, 2024. https://doi.org/10.1016/j.compbiomed.2024.109237
Copyright
Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Data Science Commons, Data Storage Systems Commons, Other Computer Engineering Commons, Other Electrical and Computer Engineering Commons, Quality Improvement Commons
Comments
NOTICE: this is the author’s version of a work that was accepted for publication in Computers in Biology and Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers in Biology and Medicine, volume 183, in 2024. https://doi.org/10.1016/j.compbiomed.2024.109237
The Creative Commons license below applies only to this version of the article.