Ensemble Deep Learning Model using Random Forest for Patient Shock Detection
DC Field | Value | Language |
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dc.contributor.author | Jeong, Minsu | - |
dc.contributor.author | 이남화 | - |
dc.contributor.author | Ko, Byuk Sung | - |
dc.contributor.author | Joe, Inwhee | - |
dc.date.accessioned | 2023-11-24T05:19:50Z | - |
dc.date.available | 2023-11-24T05:19:50Z | - |
dc.date.created | 2023-06-07 | - |
dc.date.issued | 2023-04 | - |
dc.identifier.issn | 1976-7277 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/193103 | - |
dc.description.abstract | Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient’s shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국인터넷정보학회 | - |
dc.title | Ensemble Deep Learning Model using Random Forest for Patient Shock Detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Byuk Sung | - |
dc.contributor.affiliatedAuthor | Joe, Inwhee | - |
dc.identifier.doi | 10.3837/tiis.2023.04.003 | - |
dc.identifier.scopusid | 2-s2.0-85164949984 | - |
dc.identifier.wosid | 000985494700003 | - |
dc.identifier.bibliographicCitation | KSII Transactions on Internet and Information Systems, v.17, no.4, pp.1080 - 1099 | - |
dc.relation.isPartOf | KSII Transactions on Internet and Information Systems | - |
dc.citation.title | KSII Transactions on Internet and Information Systems | - |
dc.citation.volume | 17 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1080 | - |
dc.citation.endPage | 1099 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002956067 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | SPINAL-ANESTHESIA | - |
dc.subject.keywordPlus | HYPOVOLEMIC SHOCK | - |
dc.subject.keywordPlus | HYPOTENSION | - |
dc.subject.keywordPlus | TELEMEDICINE | - |
dc.subject.keywordPlus | DECREASE | - |
dc.subject.keywordAuthor | Digital healthcare | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Random forest | - |
dc.subject.keywordAuthor | MAP | - |
dc.subject.keywordAuthor | Shock patient | - |
dc.identifier.url | https://itiis.org/digital-library/38657 | - |
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