一种机器学习方法,以改善在初级保健中基于家庭的预防性干预措施的实施监测
Cady Berkel1,2, Dillon C Knox3, Nikolaos Flemotomos3
1College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
Implementation research and practice
|October 4, 2023
概括
像TF-IDF和BERT这样的自然语言处理 (NLP) 方法可以有效地评估基于证据的育儿计划的实施,改善忠实性监测,以获得更好的儿童行为健康结果.
科学领域:
- 儿童和青少年精神病学
- 实施科学 实施科学
- 计算语言学 计算语言学
背景情况:
- 基于证据的育儿计划对于预防儿童和青少年行为健康问题至关重要.
- 扩大这些计划的规模往往会导致由于实施挑战而降低效率.
- 目前的监测方法往往是昂贵和不切实际的,特别是在资源有限的环境中.
研究的目的:
- 评估两个自然语言处理 (NLP) 方法,术语频率-反向文档频率 (TF-IDF) 和来自变压器的双向编码器表示 (BERT) 的有效性,以评估家庭检查4健康 (FCU4Health) 程序的忠实性.
- 评估这些NLP模型在主要为拉丁裔家庭服务的初级保健机构的并发和预测有效性.
主要方法:
- 这项研究利用了参与FCU4Health计划的113个家庭的成绩单.
- 用英语和西班牙语的成绩单来训练和评估TF-IDF和BERT模型.
- 模型有效性根据观察者评级 (COACH措施) 和家长参与指标进行评估.
主要成果:
- 无论是TF-IDF还是BERT都显示出与观察员评级和家长参与结果的显著关联.
- 与基线相比,NLP方法平均提高了24%的观察者评级和大约18%的家长参与指标.
- 观察者评级的平均二次误差从0.83-1.02降至0.62-0.76,而家长参与的平均误差则从0.81-27.3降至0.62-19.50.
结论:
- 自然语言处理 (NLP) 对基于证据的育儿计划中实施忠实性的低负担,及时和全面评估具有重大潜力.
- 这些发现支持NLP作为一种可扩展的解决方案,用于在现实环境中监测干预质量.
- 未来的研究方向将探索NLP在实施科学中的进一步应用和改进.
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