预测对治疗的坚持的方法和计算技术:范围审查
Beatriz Merino-Barbancho1, Ana Cipric2, Peña Arroyo1
1Universidad Politécnica de Madrid, Life Supporting Technologies Research Group, Madrid, Spain.
Computers in biology and medicine
|April 27, 2025
概括
预测患者治疗坚持性的模型对于管理慢性疾病至关重要. 本综述强调了监督学习技术,如通用线性模型和逻辑回归,作为构建这些模型的关键计算方法.
科学领域:
- 医疗保健中的计算方法
- 机器学习在医学中的应用.
- 预测模型用于患者的坚持.
背景情况:
- 治疗不坚持影响了多达50%的慢性病患者.
- 不遵守导致较差的健康结果,增加住院治疗和死亡率.
- 慢性疾病的有效管理受到患者不遵守的障碍.
研究的目的:
- 为了提供一个结构化的概述的计算方法,用于预测模型的患者治疗坚持.
- 识别和分类用于构建坚持预测模型的技术.
- 为了指导未来的研究和医疗保健建模方面的进步.
主要方法:
- 进行了范围审查.
- 搜索的数据库包括PubMed,IEEE和科学网.
- 使用主动学习的机器学习辅助管道 (ASReview) 用于初始选.
主要成果:
- 监督学习 (回归,分类) 是最常用的方法.
- 通常使用的是通用线性模型 (21.67%),后勤回归 (20%) 和随机森林 (18.33%).
- 超过54%的坚持主题与慢性代谢疾病有关;预测因素包括治疗,社会人口统计学和与疾病相关的因素.
结论:
- 准确预测治疗坚持可以改善结果和降低成本.
- 这一概述支持对坚持建模努力的理解.
- 结果指导了针对个性化治疗计划的计算方法的未来进展.
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