Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations
Abstract Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notably higher compared to the general populati...
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Nature Portfolio
2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-80792-6 |
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| author | Jae–Young Maeng JaeBin Sung Geun-Hyeong Kim Jae-Woo Kim Kyu Sun Yum Seung Park |
| author_facet | Jae–Young Maeng JaeBin Sung Geun-Hyeong Kim Jae-Woo Kim Kyu Sun Yum Seung Park |
| author_sort | Jae–Young Maeng |
| collection | DOAJ |
| description | Abstract Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notably higher compared to the general population, and delays in diagnosis can lead to irreversible neurological damage. Early diagnosis of stroke is critical to protect brain tissue and treat neurological defects. Therefore, we developed a machine learning model to diagnose stroke in patients with acute neurological manifestations in the ICU. We retrospectively collected data on patients’ underlying diseases, blood coagulation tests, procedures, and medications before neurological symptom onset from 206 patients at the Chungbuk National University Hospital ICU (July 2020–July 2022) and 45 patients at Chungnam National University Hospital between (July 2020–March 2023). Using the Categorical Boosting (CatBoost) algorithm with Bayesian optimization for hyperparameter selection and k-fold cross-validation to mitigate overfitting, we analyzed model-feature relationships with SHapley Additive exPlanations (SHAP) values. Internal model validation yielded an average accuracy of 0.7560, sensitivity of 0.8959, specificity of 0.7000, and area under the receiver operating characteristic curve (AUROC) of 0.8201. External validation yielded an accuracy of 0.7778, sensitivity of 0.7500, specificity of 0.7931, and an AUROC of 0.7328. These results demonstrated the model’s effectiveness in diagnosing stroke in non-neurological ICU patients with acute neurological manifestations using their electronic health records, making it valuable for the early detection of stroke in ICU patients. |
| format | Article |
| id | doaj-art-c819f7a6bf66472bac3778184fee97ec |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-c819f7a6bf66472bac3778184fee97ec2024-12-01T12:17:26ZengNature PortfolioScientific Reports2045-23222024-11-0114111410.1038/s41598-024-80792-6Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestationsJae–Young Maeng0JaeBin Sung1Geun-Hyeong Kim2Jae-Woo Kim3Kyu Sun Yum4Seung Park5Artificial Intelligence Center, Chungbuk National University HospitalArtificial Intelligence Center, Chungbuk National University HospitalArtificial Intelligence Center, Chungbuk National University HospitalArtificial Intelligence Center, Chungbuk National University HospitalDepartment of Neurology, Chungbuk National University Hospital and Chungbuk National University College of MedicineArtificial Intelligence Center, Chungbuk National University HospitalAbstract Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notably higher compared to the general population, and delays in diagnosis can lead to irreversible neurological damage. Early diagnosis of stroke is critical to protect brain tissue and treat neurological defects. Therefore, we developed a machine learning model to diagnose stroke in patients with acute neurological manifestations in the ICU. We retrospectively collected data on patients’ underlying diseases, blood coagulation tests, procedures, and medications before neurological symptom onset from 206 patients at the Chungbuk National University Hospital ICU (July 2020–July 2022) and 45 patients at Chungnam National University Hospital between (July 2020–March 2023). Using the Categorical Boosting (CatBoost) algorithm with Bayesian optimization for hyperparameter selection and k-fold cross-validation to mitigate overfitting, we analyzed model-feature relationships with SHapley Additive exPlanations (SHAP) values. Internal model validation yielded an average accuracy of 0.7560, sensitivity of 0.8959, specificity of 0.7000, and area under the receiver operating characteristic curve (AUROC) of 0.8201. External validation yielded an accuracy of 0.7778, sensitivity of 0.7500, specificity of 0.7931, and an AUROC of 0.7328. These results demonstrated the model’s effectiveness in diagnosing stroke in non-neurological ICU patients with acute neurological manifestations using their electronic health records, making it valuable for the early detection of stroke in ICU patients.https://doi.org/10.1038/s41598-024-80792-6Clinical decision support systemIntensive care unitNeurological manifestationStroke |
| spellingShingle | Jae–Young Maeng JaeBin Sung Geun-Hyeong Kim Jae-Woo Kim Kyu Sun Yum Seung Park Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations Scientific Reports Clinical decision support system Intensive care unit Neurological manifestation Stroke |
| title | Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations |
| title_full | Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations |
| title_fullStr | Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations |
| title_full_unstemmed | Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations |
| title_short | Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations |
| title_sort | machine learning based diagnostic model for stroke in non neurological intensive care unit patients with acute neurological manifestations |
| topic | Clinical decision support system Intensive care unit Neurological manifestation Stroke |
| url | https://doi.org/10.1038/s41598-024-80792-6 |
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