Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea
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| Publicado en: | Diagnostics vol. 11, no. 10 (2021), p. 1909 |
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| Autor Principal: | |
| Outros autores: | , , , , , , , , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 2584363437 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-4418 | ||
| 024 | 7 | |a 10.3390/diagnostics11101909 |2 doi | |
| 035 | |a 2584363437 | ||
| 045 | 2 | |b d20210101 |b d20211231 | |
| 084 | |a 231452 |2 nlm | ||
| 100 | 1 | |a Park, Dougho |u Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>parkdougho@gmail.com</email> | |
| 245 | 1 | |a Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea | |
| 260 | |b MDPI AG |c 2021 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful. | |
| 651 | 4 | |a South Korea | |
| 653 | |a Cardiovascular disease | ||
| 653 | |a Hospitals | ||
| 653 | |a Variables | ||
| 653 | |a Patients | ||
| 653 | |a Machine learning | ||
| 653 | |a Stroke | ||
| 653 | |a Datasets | ||
| 653 | |a Vein & artery diseases | ||
| 653 | |a Algorithms | ||
| 653 | |a Cardiac arrhythmia | ||
| 653 | |a Disability | ||
| 700 | 1 | |a Jeong, Eunhwan |u Department of Neurology, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>jeh132000@hanmail.net</email> (E.J.); <email>openmind-2u@hanmail.net</email> (H.K.) | |
| 700 | 1 | |a Kim, Haejong |u Department of Neurology, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>jeh132000@hanmail.net</email> (E.J.); <email>openmind-2u@hanmail.net</email> (H.K.) | |
| 700 | 1 | |a Pyun, Hae Wook |u Department of Radiology, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>biganhoo@naver.com</email> | |
| 700 | 1 | |a Kim, Haemin |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 700 | 1 | |a Yeon-Ju Choi |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 700 | 1 | |a Kim, Youngsoo |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 700 | 1 | |a Jin, Suntak |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 700 | 1 | |a Hong, Daeyoung |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 700 | 1 | |a Dong Woo Lee |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 700 | 1 | |a Su Yun Lee |u Department of Neurology, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>jeh132000@hanmail.net</email> (E.J.); <email>openmind-2u@hanmail.net</email> (H.K.) | |
| 700 | 1 | |a Mun-Chul, Kim |u Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang 37659, Korea; <email>soosungzzang@hanmail.net</email> (H.K.); <email>ns_yeonju@naver.com</email> (Y.-J.C.); <email>Youngsooooo@gmail.com</email> (Y.K.); <email>zin614@gmail.com</email> (S.J.); <email>hongdy2000@gmail.com</email> (D.H.); <email>nancbr2001@gmail.com</email> (D.W.L.) | |
| 773 | 0 | |t Diagnostics |g vol. 11, no. 10 (2021), p. 1909 | |
| 786 | 0 | |d ProQuest |t Research Library | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2584363437/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2584363437/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2584363437/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |