Multilayer perceptron-based prediction of stroke mimics in prehospital triage
Abstract The identification of stroke mimics (SMs) in patients with stroke could lead to delayed diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an accurate tool for clinical applications. However, MLP haven’t been applied in patients with suspected stroke onse...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-22919-1 |
| _version_ | 1797991851776540672 |
|---|---|
| author | Zheyu Zhang Dengfeng Zhou Jungen Zhang Yuyun Xu Gaoping Lin Bo Jin Yingchuan Liang Yu Geng Sheng Zhang |
| author_facet | Zheyu Zhang Dengfeng Zhou Jungen Zhang Yuyun Xu Gaoping Lin Bo Jin Yingchuan Liang Yu Geng Sheng Zhang |
| author_sort | Zheyu Zhang |
| collection | DOAJ |
| description | Abstract The identification of stroke mimics (SMs) in patients with stroke could lead to delayed diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an accurate tool for clinical applications. However, MLP haven’t been applied in patients with suspected stroke onset within 24 h. Here, we aimed to develop a MLP model to predict SM in patients. We retrospectively reviewed the data of patients with a prehospital diagnosis of suspected stroke between July 2017 and June 2021. SMs were confirmed during hospitalization. We included demographic information, clinical manifestations, medical history, and systolic and diastolic pressure on admission. First, the cohort was randomly divided into a training set (70%) and an external testing set (30%). Then, the least absolute shrinkage and selection operator (LASSO) method was used in feature selection and an MLP model was trained based on the selected items. Then, we evaluated the performance of the model using the ten-fold cross validation method. Finally, we used the external testing set to compare the MLP model with FABS scoring system (FABS) and TeleStroke Mimic Score (TM-Score) using a receiver operator characteristic (ROC) curve. In total, 402 patients were included. Of these, 82 (20.5%) were classified as SMs. During the ten-fold cross validation, the mean area under the ROC curve (AUC) of 10 training sets and 10 validation sets were 0.92 and 0.87, respectively. In the external testing set, the AUC of the MLP model was significantly higher than that of the FABS (0.855 vs. 0.715, P = 0.038) and TM-Score (0.855 vs. 0.646, P = 0.006). The MLP model had significantly better performance in predicting SMs than FABS and TM-Score. |
| first_indexed | 2024-04-11T08:58:41Z |
| format | Article |
| id | doaj.art-b4c6719287134e2f804a4d9329159b66 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T08:58:41Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b4c6719287134e2f804a4d9329159b662022-12-22T04:33:08ZengNature PortfolioScientific Reports2045-23222022-10-011211810.1038/s41598-022-22919-1Multilayer perceptron-based prediction of stroke mimics in prehospital triageZheyu Zhang0Dengfeng Zhou1Jungen Zhang2Yuyun Xu3Gaoping Lin4Bo Jin5Yingchuan Liang6Yu Geng7Sheng Zhang8Department of Neurology, Center for Rehabilitation Medicine, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalKey Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of TechnologyHangzhou Emergency Medical Center of Zhejiang ProvinceDepartment of Radiology, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalDepartment of Neurology, Center for Rehabilitation Medicine, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalDepartment of Neurology, Center for Rehabilitation Medicine, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalDepartment of Neurology, Center for Rehabilitation Medicine, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalDepartment of Neurology, Center for Rehabilitation Medicine, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalDepartment of Neurology, Center for Rehabilitation Medicine, People’s Hospital of Hangzhou Medical College, Zhejiang Provincial People’s HospitalAbstract The identification of stroke mimics (SMs) in patients with stroke could lead to delayed diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an accurate tool for clinical applications. However, MLP haven’t been applied in patients with suspected stroke onset within 24 h. Here, we aimed to develop a MLP model to predict SM in patients. We retrospectively reviewed the data of patients with a prehospital diagnosis of suspected stroke between July 2017 and June 2021. SMs were confirmed during hospitalization. We included demographic information, clinical manifestations, medical history, and systolic and diastolic pressure on admission. First, the cohort was randomly divided into a training set (70%) and an external testing set (30%). Then, the least absolute shrinkage and selection operator (LASSO) method was used in feature selection and an MLP model was trained based on the selected items. Then, we evaluated the performance of the model using the ten-fold cross validation method. Finally, we used the external testing set to compare the MLP model with FABS scoring system (FABS) and TeleStroke Mimic Score (TM-Score) using a receiver operator characteristic (ROC) curve. In total, 402 patients were included. Of these, 82 (20.5%) were classified as SMs. During the ten-fold cross validation, the mean area under the ROC curve (AUC) of 10 training sets and 10 validation sets were 0.92 and 0.87, respectively. In the external testing set, the AUC of the MLP model was significantly higher than that of the FABS (0.855 vs. 0.715, P = 0.038) and TM-Score (0.855 vs. 0.646, P = 0.006). The MLP model had significantly better performance in predicting SMs than FABS and TM-Score.https://doi.org/10.1038/s41598-022-22919-1 |
| spellingShingle | Zheyu Zhang Dengfeng Zhou Jungen Zhang Yuyun Xu Gaoping Lin Bo Jin Yingchuan Liang Yu Geng Sheng Zhang Multilayer perceptron-based prediction of stroke mimics in prehospital triage Scientific Reports |
| title | Multilayer perceptron-based prediction of stroke mimics in prehospital triage |
| title_full | Multilayer perceptron-based prediction of stroke mimics in prehospital triage |
| title_fullStr | Multilayer perceptron-based prediction of stroke mimics in prehospital triage |
| title_full_unstemmed | Multilayer perceptron-based prediction of stroke mimics in prehospital triage |
| title_short | Multilayer perceptron-based prediction of stroke mimics in prehospital triage |
| title_sort | multilayer perceptron based prediction of stroke mimics in prehospital triage |
| url | https://doi.org/10.1038/s41598-022-22919-1 |
| work_keys_str_mv | AT zheyuzhang multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT dengfengzhou multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT jungenzhang multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT yuyunxu multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT gaopinglin multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT bojin multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT yingchuanliang multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT yugeng multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage AT shengzhang multilayerperceptronbasedpredictionofstrokemimicsinprehospitaltriage |