MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke
Background The study aims to address the challenges of nursing decision-making and the optimization of personalized nursing plans in the management of hemorrhagic stroke. Due to the rapid progression and high complexity of hemorrhagic stroke, traditional nursing methods struggle to cope with the cha...
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| Format: | Article |
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PeerJ Inc.
2025-06-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2969.pdf |
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| author | Ting Zhou Dandan Li Jingfang Zuo Aihua Gu Li Zhao |
| author_facet | Ting Zhou Dandan Li Jingfang Zuo Aihua Gu Li Zhao |
| author_sort | Ting Zhou |
| collection | DOAJ |
| description | Background The study aims to address the challenges of nursing decision-making and the optimization of personalized nursing plans in the management of hemorrhagic stroke. Due to the rapid progression and high complexity of hemorrhagic stroke, traditional nursing methods struggle to cope with the challenges posed by its high incidence and high disability rate. Methods To address this, we propose an innovative approach based on multimodal data fusion and a non-stationary Gaussian process model. Utilizing multidimensional data from the MIMIC-IV database (including patient medical history, nursing records, laboratory test results, etc.), we developed a hybrid predictive model with a multiscale kernel transformer non-stationary Gaussian process (MSKT-NSGP) architecture to handle non-stationary time-series data and capture the dynamic changes in a patient’s condition. Results The proposed MSKT-NSGP model outperformed traditional algorithms in prediction accuracy, computational efficiency, and uncertainty handling. For hematoma expansion prediction, it achieved 85.5% accuracy, an area under the curve (AUC) of 0.87, and reduced mean squared error (MSE) by 18% compared to the sparse variational Gaussian process (SVGP). With an inference speed of 55 milliseconds per sample, it supports real-time predictions. The model maintained a confidence interval coverage near 95% with narrower widths, indicating precise uncertainty estimation. These results highlight its potential to enhance nursing decision-making, optimize personalized plans, and improve patient outcomes. |
| format | Article |
| id | doaj-art-dad0b63468c643cc8fe7e34e4b535e28 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-dad0b63468c643cc8fe7e34e4b535e282025-08-20T02:09:37ZengPeerJ Inc.PeerJ Computer Science2376-59922025-06-0111e296910.7717/peerj-cs.2969MSKT: multimodal data fusion for improved nursing management in hemorrhagic strokeTing Zhou0Dandan Li1Jingfang Zuo2Aihua Gu3Li Zhao4Jiangsu Provincial Cancer Hospital, Nanjing, Jiangsu, ChinaJiangsu Provincial Cancer Hospital, Nanjing, Jiangsu, ChinaJiangsu Provincial Cancer Hospital, Nanjing, Jiangsu, ChinaJiangsu Provincial Cancer Hospital, Nanjing, Jiangsu, ChinaSchool of Automation and Software Engineering, Shanxi University, Taiyuan, Shanxi, ChinaBackground The study aims to address the challenges of nursing decision-making and the optimization of personalized nursing plans in the management of hemorrhagic stroke. Due to the rapid progression and high complexity of hemorrhagic stroke, traditional nursing methods struggle to cope with the challenges posed by its high incidence and high disability rate. Methods To address this, we propose an innovative approach based on multimodal data fusion and a non-stationary Gaussian process model. Utilizing multidimensional data from the MIMIC-IV database (including patient medical history, nursing records, laboratory test results, etc.), we developed a hybrid predictive model with a multiscale kernel transformer non-stationary Gaussian process (MSKT-NSGP) architecture to handle non-stationary time-series data and capture the dynamic changes in a patient’s condition. Results The proposed MSKT-NSGP model outperformed traditional algorithms in prediction accuracy, computational efficiency, and uncertainty handling. For hematoma expansion prediction, it achieved 85.5% accuracy, an area under the curve (AUC) of 0.87, and reduced mean squared error (MSE) by 18% compared to the sparse variational Gaussian process (SVGP). With an inference speed of 55 milliseconds per sample, it supports real-time predictions. The model maintained a confidence interval coverage near 95% with narrower widths, indicating precise uncertainty estimation. These results highlight its potential to enhance nursing decision-making, optimize personalized plans, and improve patient outcomes.https://peerj.com/articles/cs-2969.pdfHemorrhagic strokeNursing managementNon-stationary Gaussian processMultiscale Kernel functionPredictive model |
| spellingShingle | Ting Zhou Dandan Li Jingfang Zuo Aihua Gu Li Zhao MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke PeerJ Computer Science Hemorrhagic stroke Nursing management Non-stationary Gaussian process Multiscale Kernel function Predictive model |
| title | MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke |
| title_full | MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke |
| title_fullStr | MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke |
| title_full_unstemmed | MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke |
| title_short | MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke |
| title_sort | mskt multimodal data fusion for improved nursing management in hemorrhagic stroke |
| topic | Hemorrhagic stroke Nursing management Non-stationary Gaussian process Multiscale Kernel function Predictive model |
| url | https://peerj.com/articles/cs-2969.pdf |
| work_keys_str_mv | AT tingzhou msktmultimodaldatafusionforimprovednursingmanagementinhemorrhagicstroke AT dandanli msktmultimodaldatafusionforimprovednursingmanagementinhemorrhagicstroke AT jingfangzuo msktmultimodaldatafusionforimprovednursingmanagementinhemorrhagicstroke AT aihuagu msktmultimodaldatafusionforimprovednursingmanagementinhemorrhagicstroke AT lizhao msktmultimodaldatafusionforimprovednursingmanagementinhemorrhagicstroke |