Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data
Abstract In acute ischemic stroke due to large-vessel occlusion (AIS-LVO), late-window endovascular thrombectomy (EVT) decisions depend on penumbra-to-core (P:C) mismatch from computed tomographic perfusion (CTP). We developed multiple machine learning (ML) models to predict P:C ratios from a retros...
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| Format: | Article |
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Nature Portfolio
2025-06-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01703-1 |
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| author | Shaun Kohli Parul Agarwal “Andy” Ho Wing Chan Asala Erekat Girish Nadkarni Benjamin Kummer |
| author_facet | Shaun Kohli Parul Agarwal “Andy” Ho Wing Chan Asala Erekat Girish Nadkarni Benjamin Kummer |
| author_sort | Shaun Kohli |
| collection | DOAJ |
| description | Abstract In acute ischemic stroke due to large-vessel occlusion (AIS-LVO), late-window endovascular thrombectomy (EVT) decisions depend on penumbra-to-core (P:C) mismatch from computed tomographic perfusion (CTP). We developed multiple machine learning (ML) models to predict P:C ratios from a retrospectively-identified cohort of AIS-LVO patients who underwent CTP within 30 min of initial neuroimaging, using non-imaging electronic health record (EHR) data available prior to CTP evaluation. We extracted structured data and free-text clinical notes from the EHR, generating document embeddings as sums of BioWordVec vectors weighted by term-frequency-inverse-document-frequency scores. We identified 120 patients; an extreme-gradient-boosting model classified P:C ratios as ≥ or <1.8, achieving an AUROC of 0.80 (95% CI 0.57–0.92) with optimal performance using text limited to 500 characters. Sensitivity was 0.80, specificity 0.66, and F1 score 0.86. Our findings suggest that ML models leveraging real-world non-imaging data can potentially aid LVO-AIS triage, though further validation is needed. |
| format | Article |
| id | doaj-art-2bb6b784d2364a4bb2fc30113c112da0 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-2bb6b784d2364a4bb2fc30113c112da02025-08-20T02:31:09ZengNature Portfolionpj Digital Medicine2398-63522025-06-01811810.1038/s41746-025-01703-1Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note dataShaun Kohli0Parul Agarwal1“Andy” Ho Wing Chan2Asala Erekat3Girish Nadkarni4Benjamin Kummer5Icahn School of Medicine at Mount SinaiDepartment of Neurology, Icahn School of Medicine at Mount SinaiDepartment of Neurology, Icahn School of Medicine at Mount SinaiDepartment of Neurology, Icahn School of Medicine at Mount SinaiWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount SinaiDepartment of Neurology, Icahn School of Medicine at Mount SinaiAbstract In acute ischemic stroke due to large-vessel occlusion (AIS-LVO), late-window endovascular thrombectomy (EVT) decisions depend on penumbra-to-core (P:C) mismatch from computed tomographic perfusion (CTP). We developed multiple machine learning (ML) models to predict P:C ratios from a retrospectively-identified cohort of AIS-LVO patients who underwent CTP within 30 min of initial neuroimaging, using non-imaging electronic health record (EHR) data available prior to CTP evaluation. We extracted structured data and free-text clinical notes from the EHR, generating document embeddings as sums of BioWordVec vectors weighted by term-frequency-inverse-document-frequency scores. We identified 120 patients; an extreme-gradient-boosting model classified P:C ratios as ≥ or <1.8, achieving an AUROC of 0.80 (95% CI 0.57–0.92) with optimal performance using text limited to 500 characters. Sensitivity was 0.80, specificity 0.66, and F1 score 0.86. Our findings suggest that ML models leveraging real-world non-imaging data can potentially aid LVO-AIS triage, though further validation is needed.https://doi.org/10.1038/s41746-025-01703-1 |
| spellingShingle | Shaun Kohli Parul Agarwal “Andy” Ho Wing Chan Asala Erekat Girish Nadkarni Benjamin Kummer Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data npj Digital Medicine |
| title | Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data |
| title_full | Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data |
| title_fullStr | Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data |
| title_full_unstemmed | Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data |
| title_short | Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data |
| title_sort | machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data |
| url | https://doi.org/10.1038/s41746-025-01703-1 |
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