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Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam
DC Field | Value | Language |
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dc.contributor.author | 강선주 | - |
dc.contributor.author | 김광현 | - |
dc.date.accessioned | 2023-03-03T02:12:11Z | - |
dc.date.available | 2023-03-03T02:12:11Z | - |
dc.date.issued | 2022-11 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/192767 | - |
dc.description.abstract | Introduction: In this study, we developed a simplified artificial intelligence to support the clinical decision-making of medical personnel in a resource-limited setting. Methods: We selected seven infectious disease categories that impose a heavy disease burden in the central Vietnam region: mosquito-borne disease, acute gastroenteritis, respiratory tract infection, pulmonary tuberculosis, sepsis, primary nervous system infection, and viral hepatitis. We developed a set of questionnaires to collect information on the current symptoms and history of patients suspected to have infectious diseases. We used data collected from 1,129 patients to develop and test a diagnostic model. We used XGBoost, LightGBM, and CatBoost algorithms to create artificial intelligence for clinical decision support. We used a 4-fold cross-validation method to validate the artificial intelligence model. After 4-fold cross-validation, we tested artificial intelligence models on a separate test dataset and estimated diagnostic accuracy for each model. Results: We recruited 1,129 patients for final analyses. Artificial intelligence developed by the CatBoost algorithm showed the best performance, with 87.61% accuracy and an F1-score of 87.71. The F1-score of the CatBoost model by disease entity ranged from 0.80 to 0.97. Diagnostic accuracy was the lowest for sepsis and the highest for central nervous system infection. Conclusion: Simplified artificial intelligence could be helpful in clinical decision support in settings with limited resources. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Frontiers Editorial Office | - |
dc.relation.isPartOf | FRONTIERS IN PUBLIC HEALTH | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Artificial Intelligence | - |
dc.subject.MESH | Communicable Diseases* / diagnosis | - |
dc.subject.MESH | Decision Support Systems, Clinical* | - |
dc.subject.MESH | Hospitals | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Pilot Projects | - |
dc.subject.MESH | Sepsis* / diagnosis | - |
dc.subject.MESH | Sepsis* / therapy | - |
dc.subject.MESH | Surveys and Questionnaires | - |
dc.subject.MESH | Vietnam | - |
dc.title | Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam | - |
dc.type | Article | - |
dc.contributor.college | Graduate School of Public Health (보건대학원) | - |
dc.contributor.department | Graduate School of Public Health (보건대학원) | - |
dc.contributor.googleauthor | Kwanghyun Kim | - |
dc.contributor.googleauthor | Myung-Ken Lee | - |
dc.contributor.googleauthor | Hyun Kyung Shin | - |
dc.contributor.googleauthor | Hyunglae Lee | - |
dc.contributor.googleauthor | Boram Kim | - |
dc.contributor.googleauthor | Sunjoo Kang | - |
dc.identifier.doi | 10.3389/fpubh.2022.1023098 | - |
dc.contributor.localId | A05958 | - |
dc.contributor.localId | A06087 | - |
dc.relation.journalcode | J03763 | - |
dc.identifier.eissn | 2296-2565 | - |
dc.identifier.pmid | 36438286 | - |
dc.subject.keyword | Asia Southeastern | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | communicable diseases | - |
dc.subject.keyword | international health | - |
dc.subject.keyword | low- & middle-income countries | - |
dc.contributor.alternativeName | Kang, Sunjoo | - |
dc.contributor.affiliatedAuthor | 강선주 | - |
dc.contributor.affiliatedAuthor | 김광현 | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 1023098 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN PUBLIC HEALTH, Vol.10 : 1023098, 2022-11 | - |
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