CheXMed: A multimodal learning algorithm for pneumonia detection in the elderly

Hao REN, Fengshi JING, Zhurong CHEN, Shan HE, Jiandong ZHOU, Le LIU, Ran JING, Wanmin LIAN, Junzhang TIAN, Qingpeng ZHANG, Zhongzhi XU, Weibin CHENG

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)

Abstract

Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age groups. This study (a) investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and (b) developed and tested a multimodal model called CheXMed that incorporates clinical notes together with image data to improve pneumonia detection performance for older people. Accuracy, precision, recall, and F1-score were used for model performance evaluation. CheXMed outperforms baseline models on all evaluation metrics. The accuracy, precision, recall, and F1-score are 0.746, 0.746, 0.740, 0.743 for CheXMed, 0.645, 0.680, 0.535, 0.599 for CheXNet, 0.623, 0.655, 0.521, 0.580 for DenseNet121, and 0.610, 0.617, 0.543, 0.577 for ResNet18. Copyright © 2023 Elsevier Inc. All rights reserved.

Original languageEnglish
Article number119854
JournalInformation Sciences
Volume654
DOIs
Publication statusPublished - Jan 2024

Citation

Ren, H., Jing, F., Chen, Z., He, S., Zhou, J., Liu, L., Jing, R., Lian, W., Tian, J., Zhang, Q., Xu, Z., & Cheng, W. (2024). CheXMed: A multimodal learning algorithm for pneumonia detection in the elderly. Information Sciences, 654, Article 119854. https://doi.org/10.1016/j.ins.2023.119854

Keywords

  • PG student publication

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