机器学习模型用于预测乳腺癌中的辐射皮肤炎:一个范围审查
Jayana Castelo Branco Cavalcante de Meneses1, Abilio Torres Dos Santos Neto1, Maria Aparecida Ferreira Domingos1
1Faculty of Nursing, Federal University of Ceará, Fortaleza.
Computers, informatics, nursing : CIN
|January 30, 2026
概括
机器学习模型在预测乳腺癌患者的辐射皮肤炎方面表现有前途. 对多原子生物标志物和护理数据的进一步研究对于改进这些预测工具至关重要.
科学领域:
- 在瘤学瘤学.
- 辐射疗法 辐射疗法
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 通过预测不利条件来改善健康结果的潜力.
- 在乳腺癌患者中,ML模型可以个性化预防辐射诱导皮肤毒性的预防策略.
研究的目的:
- 对机器学习模型进行范围审查,以预测正在接受乳腺癌治疗的妇女的辐射皮肤炎.
- 在预测这种常见的副作用方面探索ML应用的当前景观.
主要方法:
- 在2023年11月,对7个电子数据库和灰色文献进行了全面的搜索.
- 为了出版物选择和搜索扩展,使用RAYYAN参考管理器和ResearchRabbit软件.
- 没有对出版年限的限制.
主要成果:
- 审查中包括了22个出版物.
- 大多数模型使用临床预测因素和交叉验证预测了急性辐射皮肤炎.
- 随机森林是占主导地位的算法,而包含各种预测因素的贝叶斯网络模型显示出卓越的性能.
结论:
- 对多原子生物标志物的进一步研究对于提高预测准确性至关重要.
- 建议建立最低护理数据库,以支持可靠的预测模型开发.
- 持续的研究对于推进放射性皮肤炎管理的个性化护理至关重要.
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