Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke
Abstract In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Existing predictive models typically estimate risk at one, early time point, failing to account for dynamic variables. To address this, we deve...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01687-y |
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| _version_ | 1850154503002652672 |
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| author | Ethan Phillips Odhran O’Donoghue Yumeng Zhang Panos Tsimpos Leigh Ann Mallinger Stefanos Chatzidakis Jack Pohlmann Yili Du Ivy Kim Jonathan Song Benjamin Brush Stelios Smirnakis Charlene J. Ong Agni Orfanoudaki |
| author_facet | Ethan Phillips Odhran O’Donoghue Yumeng Zhang Panos Tsimpos Leigh Ann Mallinger Stefanos Chatzidakis Jack Pohlmann Yili Du Ivy Kim Jonathan Song Benjamin Brush Stelios Smirnakis Charlene J. Ong Agni Orfanoudaki |
| author_sort | Ethan Phillips |
| collection | DOAJ |
| description | Abstract In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Existing predictive models typically estimate risk at one, early time point, failing to account for dynamic variables. To address this, we developed Hybrid Ensemble Learning Models for Edema Trajectory (HELMET) to predict midline shift severity, an established indicator of malignant edema, over 8-h and 24-h windows. The HELMET models were trained on retrospective data from 623 patients and validated on 63 patients from a different hospital system, achieving mean areas under the receiver operating characteristic curve of 96.6% and 92.5%, respectively. By integrating transformer-based large language models with supervised ensemble learning, HELMET demonstrates the value of combining clinician expertise with multimodal health records in assessing patient risk. Our approach provides a framework for accurate, real-time estimation of dynamic clinical targets using human-curated and algorithm-derived inputs. |
| format | Article |
| id | doaj-art-e03065317dfb40e19a923851c1ab26bb |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-e03065317dfb40e19a923851c1ab26bb2025-08-20T02:25:17ZengNature Portfolionpj Digital Medicine2398-63522025-05-018111410.1038/s41746-025-01687-yHybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery strokeEthan Phillips0Odhran O’Donoghue1Yumeng Zhang2Panos Tsimpos3Leigh Ann Mallinger4Stefanos Chatzidakis5Jack Pohlmann6Yili Du7Ivy Kim8Jonathan Song9Benjamin Brush10Stelios Smirnakis11Charlene J. Ong12Agni Orfanoudaki13University of OxfordUniversity of OxfordNorth Carolina State UniversityMassachusetts Institute of TechnologyBoston Medical Center, Department of NeurologyBrigham & Women’s Hospital, Department of NeurologyBoston Medical Center, Department of NeurologyBoston University School of Public HealthBoston Medical Center, Department of NeurologyBoston University Chobanian & Avedisian School of MedicineNYU Langone HospitalBrigham & Women’s Hospital, Department of NeurologyBoston Medical Center, Department of NeurologyUniversity of OxfordAbstract In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Existing predictive models typically estimate risk at one, early time point, failing to account for dynamic variables. To address this, we developed Hybrid Ensemble Learning Models for Edema Trajectory (HELMET) to predict midline shift severity, an established indicator of malignant edema, over 8-h and 24-h windows. The HELMET models were trained on retrospective data from 623 patients and validated on 63 patients from a different hospital system, achieving mean areas under the receiver operating characteristic curve of 96.6% and 92.5%, respectively. By integrating transformer-based large language models with supervised ensemble learning, HELMET demonstrates the value of combining clinician expertise with multimodal health records in assessing patient risk. Our approach provides a framework for accurate, real-time estimation of dynamic clinical targets using human-curated and algorithm-derived inputs.https://doi.org/10.1038/s41746-025-01687-y |
| spellingShingle | Ethan Phillips Odhran O’Donoghue Yumeng Zhang Panos Tsimpos Leigh Ann Mallinger Stefanos Chatzidakis Jack Pohlmann Yili Du Ivy Kim Jonathan Song Benjamin Brush Stelios Smirnakis Charlene J. Ong Agni Orfanoudaki Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke npj Digital Medicine |
| title | Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke |
| title_full | Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke |
| title_fullStr | Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke |
| title_full_unstemmed | Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke |
| title_short | Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke |
| title_sort | hybrid machine learning for real time prediction of edema trajectory in large middle cerebral artery stroke |
| url | https://doi.org/10.1038/s41746-025-01687-y |
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