通过与互联网搜索引擎的互动来识别肌缩性侧面硬化症
Elad Yom-Tov1, Indu Navar2, Ernest Fraenkel3
1Microsoft Research, Herzeliya, Israel.
Muscle & nerve
|October 25, 2023
概括
互联网搜索数据可以帮助识别患有肌缩侧面硬化症 (ALS) 的个体,这是一种运动神经元疾病. 这种方法可能有助于更早的查和诊断,可能减少典型的一年延迟.
科学领域:
- 神经学 神经学
- 数据科学数据科学数据科学
- 数字健康数字健康
背景情况:
- 肌缩侧面硬化症 (ALS) 的诊断主要是临床的,通常涉及症状发作的显著延迟.
- 早期发现ALS对于及时干预和管理至关重要.
- 互联网搜索引擎交互代表了健康相关研究的新型数据源.
研究的目的:
- 调查使用互联网搜索引擎查询来识别患有ALS的人的潜力.
- 评估搜索查询模式是否可以将ALS患者与健康对照者和患有疾病的患者区分开来.
- 在前性环境中评估预测模型的有效性.
主要方法:
- 分析了285名被诊断为ALS的个体的匿名搜索数据.
- 将ALS搜索数据与对照组和患有ALS疾病模仿的个体进行了比较.
- 开发了一个预测模型,并使用潜在的搜索数据进行了验证.
主要成果:
- 该模型成功地将ALS患者与对照患者区分开来,AUC为0.81.
- 在ALS患者和疾病模仿者之间观察到显著差异 (p < .05).
- 前性验证的AUC值为0.74,表明模型的有效性.
结论:
- 搜索引擎互动显示为ALS查工具的承诺.
- 利用搜索数据可能会减少ALS的诊断延迟.
- 进一步研究用于ALS识别的数字足迹是有必要的.
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