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一种用于准确和快速预测癌症发病率的新型模型
Mahmoud Hamed1,2, Berlanty A Zayed3, Fotouh R Mansour4,5
1Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km 28 Ismailia Road, Cairo, 44971, Egypt.
BMC public health
|May 6, 2025
概括
这项研究使用谷歌趋势数据引入了一种新的癌症发病率预测模型. 该模型准确地预测了美国所有50个州和54个国家的癌症率,有助于公共卫生规划.
科学领域:
- 在瘤学瘤学.
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 准确的癌症发病率预测对于有效的健康规划和资源分配至关重要.
- 传统方法依赖于历史数据,假设静态模式.
- 在准确预测未来的癌症发病率方面仍然存在挑战.
研究的目的:
- 开发和验证一种用于预测癌症发病率的新型模型.
- 评估模型在不同地理区域 (美国各州和国家) 的准确性.
- 为改善流行病学预测利用实时公共利益数据.
主要方法:
- 使用了谷歌趋势数据 (针对"癌症"的相对搜索量指数) 从2017-2023.
- 综合实际癌症发病率与RSVI的年度变化.
- 应用该模型来预测所有50个美国州和54个国家的癌症发病率.
主要成果:
- 该模型在预测美国各州 (2017-2020年) 癌症发病率方面取得了很高的准确性,误差一般低于6%.
- 在预测和实际的癌症发病率之间观察到强烈的相关性.
- 国际癌症发病率的预测准确度也相似.
结论:
- 开发的模型提供了准确的短期癌症发病率预测.
- 自2017年以来,该模式的成功跨越了美国所有50个州和54个国家.
- 这种方法为公共卫生监测和规划提供了有价值的工具.
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