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

TitleCheXMed: A multimodal learning algorithm for pneumonia detection in the elderly
Authors
KeywordsAI-assisted precision medicine
Deep neural networks
Medical image processing
Multimodal learning
Pneumonia detection
Issue Date2024
Citation
Information Sciences, 2024, v. 654, article no. 119854 How to Cite?
AbstractPneumonia 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.
Persistent Identifierhttp://hdl.handle.net/10722/336955
ISSN
2022 Impact Factor: 8.1
2023 SCImago Journal Rankings: 2.238
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorRen, Hao-
dc.contributor.authorJing, Fengshi-
dc.contributor.authorChen, Zhurong-
dc.contributor.authorHe, Shan-
dc.contributor.authorZhou, Jiandong-
dc.contributor.authorLiu, Le-
dc.contributor.authorJing, Ran-
dc.contributor.authorLian, Wanmin-
dc.contributor.authorTian, Junzhang-
dc.contributor.authorZhang, Qingpeng-
dc.contributor.authorXu, Zhongzhi-
dc.contributor.authorCheng, Weibin-
dc.date.accessioned2024-02-29T06:57:41Z-
dc.date.available2024-02-29T06:57:41Z-
dc.date.issued2024-
dc.identifier.citationInformation Sciences, 2024, v. 654, article no. 119854-
dc.identifier.issn0020-0255-
dc.identifier.urihttp://hdl.handle.net/10722/336955-
dc.description.abstractPneumonia 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.-
dc.languageeng-
dc.relation.ispartofInformation Sciences-
dc.subjectAI-assisted precision medicine-
dc.subjectDeep neural networks-
dc.subjectMedical image processing-
dc.subjectMultimodal learning-
dc.subjectPneumonia detection-
dc.titleCheXMed: A multimodal learning algorithm for pneumonia detection in the elderly-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ins.2023.119854-
dc.identifier.scopuseid_2-s2.0-85175708306-
dc.identifier.volume654-
dc.identifier.spagearticle no. 119854-
dc.identifier.epagearticle no. 119854-
dc.identifier.isiWOS:001108716500001-

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