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Prediction of ROSC After Cardiac Arrest Using Machine Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, Nan | - |
dc.contributor.author | Ho, Andrew Fu Wah | - |
dc.contributor.author | Pek, Pin Pin | - |
dc.contributor.author | Lu, Tsung-Chien | - |
dc.contributor.author | Khruekarnchana, Pairoj | - |
dc.contributor.author | Song, Kyoung Jun | - |
dc.contributor.author | Tanaka, Hideharu | - |
dc.contributor.author | Naroo, Ghulam Yasin | - |
dc.contributor.author | Gan, Han Nee | - |
dc.contributor.author | Koh, Zhi Xiong | - |
dc.contributor.author | Ma, Huei-Ming | - |
dc.contributor.author | Ong, Marcus | - |
dc.date.accessioned | 2022-10-20T02:26:28Z | - |
dc.date.available | 2022-10-20T02:26:28Z | - |
dc.date.created | 2022-10-13 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.citation | DIGITAL PERSONALIZED HEALTH AND MEDICINE, Vol.270, pp.1357-1358 | - |
dc.identifier.issn | 0926-9630 | - |
dc.identifier.uri | https://hdl.handle.net/10371/186570 | - |
dc.description.abstract | Out-of-hospital cardiac arrest (OHCA) is an important public health problem, with very low survival rate. In treating OHCA patients, the return of spontaneous circulation (ROSC) represents the success of early resuscitation efforts. In this study, we developed a machine learning model to predict ROSC and compared it with the ROSC after cardiac arrest (RACA) score. Results demonstrated the usefulness of machine learning in deriving predictive models. | - |
dc.language | 영어 | - |
dc.publisher | IOS PRESS | - |
dc.title | Prediction of ROSC After Cardiac Arrest Using Machine Learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.3233/SHTI200440 | - |
dc.citation.journaltitle | DIGITAL PERSONALIZED HEALTH AND MEDICINE | - |
dc.identifier.wosid | 000625278800323 | - |
dc.identifier.scopusid | 2-s2.0-85086911619 | - |
dc.citation.endpage | 1358 | - |
dc.citation.startpage | 1357 | - |
dc.citation.volume | 270 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Song, Kyoung Jun | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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