Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye
Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2025
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| Online Access: | https://hdl.handle.net/1721.1/158299 |
| _version_ | 1826210078885150720 |
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| author | Khaniyev, Taghi Cekic, Efecan Gecici, Neslihan Nisa Can, Sinem Ata, Naim Ulgu, Mustafa Mahir Birinci, Suayip Isikay, Ahmet Ilkay Bakir, Abdurrahman Arat, Anil Hanalioglu, Sahin |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Khaniyev, Taghi Cekic, Efecan Gecici, Neslihan Nisa Can, Sinem Ata, Naim Ulgu, Mustafa Mahir Birinci, Suayip Isikay, Ahmet Ilkay Bakir, Abdurrahman Arat, Anil Hanalioglu, Sahin |
| author_sort | Khaniyev, Taghi |
| collection | MIT |
| description | Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods: Retrospective cohort of 29,274 SAH patients, identified through national EHR system from January 2017 to December 2022, was analyzed, with mortality data obtained from central civil registration system in Türkiye. Variables included (n = 102) pre- (n = 65) and post-admission (n = 37) data, such as patient demographics, clinical presentation, comorbidities, laboratory results, and complications. We employed logistic regression (LR), decision trees (DTs), random forests (RFs), and artificial neural networks (ANN). Model performance was evaluated using area under the curve (AUC), average precision, and accuracy. Feature significance analysis was conducted using LR. Results: The average age was 56.23 ± 16.45 years (47.8% female). The overall mortality rate was 22.8% at 1 month and 33.3% at 1 year. One-month mortality increased from 20.9% to 24.57% (p < 0.001), and 1-year mortality rose from 30.85% to 35.55% (p < 0.001) in the post-COVID period compared to the pre-COVID period. For 1-month mortality prediction, the ANN, LR, RF, and DT models achieved AUCs of 0.946, 0.942, 0.931, and 0.916, with accuracies of 0.905, 0.901, 0.893, and 0.885, respectively. For 1-year mortality, the AUCs were 0.941, 0.927, 0.926, and 0.907, with accuracies of 0.884, 0.875, 0.861, and 0.851, respectively. Key predictors of mortality included age, cardiopulmonary arrest, abnormal laboratory results (such as abnormal glucose and lactate levels) at presentation, and pre-existing comorbidities. Incorporating post-admission features (n = 37) alongside pre-admission features (n = 65) improved model performance for both 1-month and 1-year mortality predictions, with average AUC improvements of 0.093 ± 0.011 and 0.089 ± 0.012, respectively. Conclusions: Our study demonstrates the effectiveness of ML models in predicting mortality in SAH patients using big data. LR models’ robustness, interpretability, and feature significance analysis validate its importance. Including post-admission data significantly improved all models’ performances. Our results demonstrate the utility of big data analytics in population-level health outcomes studies. |
| first_indexed | 2025-03-10T12:27:28Z |
| format | Article |
| id | mit-1721.1/158299 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2025-03-10T12:27:28Z |
| publishDate | 2025 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1582992025-03-04T18:14:49Z Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye Khaniyev, Taghi Cekic, Efecan Gecici, Neslihan Nisa Can, Sinem Ata, Naim Ulgu, Mustafa Mahir Birinci, Suayip Isikay, Ahmet Ilkay Bakir, Abdurrahman Arat, Anil Hanalioglu, Sahin Sloan School of Management Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods: Retrospective cohort of 29,274 SAH patients, identified through national EHR system from January 2017 to December 2022, was analyzed, with mortality data obtained from central civil registration system in Türkiye. Variables included (n = 102) pre- (n = 65) and post-admission (n = 37) data, such as patient demographics, clinical presentation, comorbidities, laboratory results, and complications. We employed logistic regression (LR), decision trees (DTs), random forests (RFs), and artificial neural networks (ANN). Model performance was evaluated using area under the curve (AUC), average precision, and accuracy. Feature significance analysis was conducted using LR. Results: The average age was 56.23 ± 16.45 years (47.8% female). The overall mortality rate was 22.8% at 1 month and 33.3% at 1 year. One-month mortality increased from 20.9% to 24.57% (p < 0.001), and 1-year mortality rose from 30.85% to 35.55% (p < 0.001) in the post-COVID period compared to the pre-COVID period. For 1-month mortality prediction, the ANN, LR, RF, and DT models achieved AUCs of 0.946, 0.942, 0.931, and 0.916, with accuracies of 0.905, 0.901, 0.893, and 0.885, respectively. For 1-year mortality, the AUCs were 0.941, 0.927, 0.926, and 0.907, with accuracies of 0.884, 0.875, 0.861, and 0.851, respectively. Key predictors of mortality included age, cardiopulmonary arrest, abnormal laboratory results (such as abnormal glucose and lactate levels) at presentation, and pre-existing comorbidities. Incorporating post-admission features (n = 37) alongside pre-admission features (n = 65) improved model performance for both 1-month and 1-year mortality predictions, with average AUC improvements of 0.093 ± 0.011 and 0.089 ± 0.012, respectively. Conclusions: Our study demonstrates the effectiveness of ML models in predicting mortality in SAH patients using big data. LR models’ robustness, interpretability, and feature significance analysis validate its importance. Including post-admission data significantly improved all models’ performances. Our results demonstrate the utility of big data analytics in population-level health outcomes studies. 2025-03-04T18:14:47Z 2025-03-04T18:14:47Z 2025-02-10 2025-02-25T13:05:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/158299 Khaniyev, T.; Cekic, E.; Gecici, N.N.; Can, S.; Ata, N.; Ulgu, M.M.; Birinci, S.; Isikay, A.I.; Bakir, A.; Arat, A.; et al. Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye. J. Clin. Med. 2025, 14, 1144. PUBLISHER_CC http://dx.doi.org/10.3390/jcm14041144 Journal of Clinical Medicine Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Khaniyev, Taghi Cekic, Efecan Gecici, Neslihan Nisa Can, Sinem Ata, Naim Ulgu, Mustafa Mahir Birinci, Suayip Isikay, Ahmet Ilkay Bakir, Abdurrahman Arat, Anil Hanalioglu, Sahin Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye |
| title | Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye |
| title_full | Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye |
| title_fullStr | Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye |
| title_full_unstemmed | Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye |
| title_short | Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye |
| title_sort | predicting mortality in subarachnoid hemorrhage patients using big data and machine learning a nationwide study in turkiye |
| url | https://hdl.handle.net/1721.1/158299 |
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