使用随机森林机器学习对来自普通人口的大型代表队伍的数据进行随机森林机器学习,可以改善临床螺旋计参考
Kris Kristensen1, Pernille H Olesen1, Anna K Roerbaek1
1Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
The clinical respiratory journal
|July 14, 2023
概括
随机森林 (RF) 模型显著改善了强迫生命能力 (FVC) 和一秒钟内强迫呼吸量 (FEV1) 肺功能的预测. 这种机器学习方法提高了螺旋计的诊断准确性,可能减少慢性阻塞性肺病 (COPD) 的误诊.
科学领域:
- 肺部医学和呼吸系统研究
- 生物统计学和机器学习应用程序
- 公共卫生和流行病学
背景情况:
- 螺旋测量是诊断COPD等肺部疾病的关键工具,但它面临着导致误诊的挑战.
- 现有的强迫生命能力 (FVC) 和一秒内强迫呼气量 (FEV1) 的临床参考值可能对不同人群缺乏准确性.
- 本研究探讨了随机森林 (RF) 算法的实用性,以使用大型,代表性的美国人口数据集来改进FVC和FEV1预测模型.
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