지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.5
- 수록면
- 251 - 254 (4page)
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초록· 키워드
오류제보하기
Machine learning models can be particularly useful in the survival analysis of cancer patients based on their clinical data, as they can identify different patterns and their relationships that are very difficult to predict using Old Statistical Tools. Due to the survival analysis, doctors can go through different patients about their medical histories and take more effective decisions, which helps them to make better future strategies for patients’ treatment while doing the whole treatment process. Survival analysis also assists medical researchers in identifying a few different treatment approaches that could be used to predict patient outcomes or develop new therapies. We also did the survival analysis of gastric cancer patients using their clinical data using different machine learning models. We aim to provide a model which assists medical stakeholders in deciding on the behalf of survival analysis for future patients treatment and follow up strategies.
목차
ABSTRACT
I.Introduction
Ⅱ.Materials and Methods
Ⅳ.Results and Discussions
VI. Conclusion
References
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