在社区居住的成年人中,糖尿病前期的危险因素:一种概括估计方程逻辑回归方法,与自然语言处理见解
Aeri Kim1, Eunjoo Jeon2, Hana Lee1
1College of Nursing, Seoul National University, Seoul, South Korea.
Research in nursing & health
|July 4, 2024
概括
一个新的算法可以使用护理记录在成年人中识别高风险的糖尿病前期. 女性的性别,年龄,癌症保险和护理人员是关键的风险因素,使早期干预成为可能.
科学领域:
- 公共卫生 公共卫生
- 老年学是一门学科.
- 医疗信息学 医疗信息学
背景情况:
- 糖尿病前期影响全球数以百万计的人,患糖尿病的进展率很高.
- 早期识别和干预对于管理糖尿病前期和预防糖尿病并发症至关重要.
研究的目的:
- 通过在护理记录中检测到的症状来对生活在社区中的成年人进行糖尿病前期高风险分类.
- 识别与糖尿病前期风险相关的个人,社会和临床特征.
主要方法:
- 通过使用自然语言处理 (NLP) 算法,分析了来自1628名成年人的26,840份家庭护理护理记录.
- 根据症状检测,将哺乳期分为糖尿病前期的低风险和高风险.
- 统计分析包括t测试,奇平方测试,后勤回归和数据可视化以确定风险因素.
主要成果:
- 12.18%的哺乳期 (3270) 被归类为糖尿病前期高风险.
- 在风险组之间观察到个人,社会和临床因素的显著差异.
- 女性性别,年龄较大,癌症覆盖率和家庭护理人员的存在与糖尿病前期风险增加有关.
结论:
- 一个NLP算法可以有效地检测在社区环境中患糖尿病前期高风险的个体.
- 识别特定的风险因素,如女性的性别,年龄,保险覆盖范围和护理人员身份,有助于针对性干预.
- 基于这种方法的积极监测和早期干预策略可以改善健康结果并降低医疗保健成本.
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