Zhi, Zhuo;
Elbadawi, Moe;
Daneshmend, Adam;
Orlu, Mine;
Basit, Abdul;
Demosthenous, Andreas;
Rodrigues, Miguel;
(2022)
Multimodal Diagnosis for Pulmonary Embolism from EHR Data and CT Images.
In:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
(pp. pp. 2053-2057).
IEEE: Glasgow, Scotland, United Kingdom.
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Abstract
Pulmonary Embolism (PE) is a severe medical condition that can pose a significant risk to life. Traditional deep learning methods for PE diagnosis are based on Computed Tomography (CT) images and do not consider the patient's clinical context. To make full use of patient's clinical information, this article presents a multimodal fusion model ingesting Electronic Health Record (EHR) data and CT images for PE diagnosis. The proposed model is based on multilayer perception and convolutional neural networks. To remove the invalid information in the EHR data, the multidimensional scaling algorithm is performed for feature dimension reduction. The EHR data and CT images of 600 patients are used for experiments. The experiment results show that the proposed models outperform existing methods and the multimodal fusion model shows better performance than the single-input model.
| Type: | Proceedings paper |
|---|---|
| Title: | Multimodal Diagnosis for Pulmonary Embolism from EHR Data and CT Images |
| Event: | 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
| Dates: | 11 Jul 2022 - 15 Jul 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/embc48229.2022.9871041 |
| Publisher version: | https://doi.org/10.1109/EMBC48229.2022.9871041 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Solid modeling, Three-dimensional displays, Medical conditions, Computational modeling, Computed tomography, Biological system modeling, Pulmonary diseases |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155835 |
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