让最终用户开发一种新的算法来处理电子药物坚持监测设备数据
Meghan E McGrady1,2,3, Kevin A Hommel1,3, Constance A Mara1,3
1Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, United States.
JAMIA open
|November 3, 2025
概括
开发了一种算法来处理电子粘附监测装置 (EAMD) 数据,实现高精度. 最终用户发现该算法很有价值,为未来的软件开发提供反,以增强坚持科学.
科学领域:
- 坚持科学科学的坚持.
- 医疗信息学 医疗信息学
- 算法开发的发展算法.
背景情况:
- 准确的坚持数据对于临床试验分析至关重要.
- 电子粘附监测设备 (EAMD) 产生需要处理的复杂数据.
- 需要标准化的方法来将EAMD输出转换为可用的粘附数据.
研究的目的:
- 开发和评估一种算法,用于将电子粘附监测设备 (EAMD) 的输出转换为粘附数据.
- 让最终用户参与算法开发和评估过程.
- 评估算法的准确性和最终用户的满意度.
主要方法:
- 采用了四个阶段的方法:过程映射采访,焦点小组,算法参数定义和编码在R (OncMAP),和试点测试.
- 将算法生成的数据与手动重新编码的数据进行比较,以确定灵敏度,特异性和准确性.
- 收集终端用户对软件开发的感知价值和基本特征的反.
主要成果:
- 开发了一个R包 (OncMAP),用于处理EAMD数据的参数化决策规则.
- 该算法在对完整的观测进行分类时显示出100%的灵敏度和特异性,曲线下的面积为1.00.
- 试点测试人员表达了强烈的兴趣 (净发起人得分 = 71%) 并为软件改进提供了关键的反.
结论:
- 基于规则的算法可以准确地自动处理EAMD执行数据.
- 开发的算法显示了高灵敏度,特异性和准确性,提高了数据的严格性.
- 终端用户的参与对于开发一套强大的软件包至关重要,该软件包可以推进坚持科学.
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