Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records
Predicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study pro...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020099 |
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| author | Abdulrahman Al-Dailami Hulin Kuang Jianxin Wang |
| author_facet | Abdulrahman Al-Dailami Hulin Kuang Jianxin Wang |
| author_sort | Abdulrahman Al-Dailami |
| collection | DOAJ |
| description | Predicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges. The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data, enabling the effective and explainable fusion of the incomplete features. Modality-specific encoders are employed to encode each modality feature separately. To tackle the variability and incompleteness of input features among patients, a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs. Furthermore, a MultiModal Gated Contrastive Representation Learning (MMGCRL) method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance. We evaluate the proposed framework using the large-scale ICU dataset, MIMIC-III. Experimental results demonstrate its effectiveness in mortality prediction, outperforming several state-of-the-art methods. |
| format | Article |
| id | doaj-art-8a72aa3bb2d14f3083ba3f9820fb4691 |
| institution | Kabale University |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-8a72aa3bb2d14f3083ba3f9820fb46912025-08-20T03:36:12ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-06-018493395010.26599/BDMA.2024.9020099Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical RecordsAbdulrahman Al-Dailami0Hulin Kuang1Jianxin Wang2Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China, and also with Faculty of Computer and Information Technology, Sana’a University, Sana’a 999101, YemenHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaPredicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges. The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data, enabling the effective and explainable fusion of the incomplete features. Modality-specific encoders are employed to encode each modality feature separately. To tackle the variability and incompleteness of input features among patients, a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs. Furthermore, a MultiModal Gated Contrastive Representation Learning (MMGCRL) method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance. We evaluate the proposed framework using the large-scale ICU dataset, MIMIC-III. Experimental results demonstrate its effectiveness in mortality prediction, outperforming several state-of-the-art methods.https://www.sciopen.com/article/10.26599/BDMA.2024.9020099multimodal representation learninggraph neural network (gnn)mortality predictionelectronic medical records (emr)intensive care unit (icu) |
| spellingShingle | Abdulrahman Al-Dailami Hulin Kuang Jianxin Wang Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records Big Data Mining and Analytics multimodal representation learning graph neural network (gnn) mortality prediction electronic medical records (emr) intensive care unit (icu) |
| title | Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records |
| title_full | Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records |
| title_fullStr | Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records |
| title_full_unstemmed | Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records |
| title_short | Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records |
| title_sort | multimodal representation learning based on personalized graph based fusion for mortality prediction using electronic medical records |
| topic | multimodal representation learning graph neural network (gnn) mortality prediction electronic medical records (emr) intensive care unit (icu) |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020099 |
| work_keys_str_mv | AT abdulrahmanaldailami multimodalrepresentationlearningbasedonpersonalizedgraphbasedfusionformortalitypredictionusingelectronicmedicalrecords AT hulinkuang multimodalrepresentationlearningbasedonpersonalizedgraphbasedfusionformortalitypredictionusingelectronicmedicalrecords AT jianxinwang multimodalrepresentationlearningbasedonpersonalizedgraphbasedfusionformortalitypredictionusingelectronicmedicalrecords |