在2型糖尿病患者中,使用最少绝对收缩和选择操作员回归和机器学习算法来评估患上高尿素血的风险
Qingquan Chen1,2, Haiping Hu1,2, Qing He1,2
1The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China.
高尿路血是2型糖尿病的常见并发症. 一个人工神经网络模型确定了关键的风险因素,有助于在糖尿病患者中早期发现和管理高尿路血症.
科学领域:
- 内分泌学和新陈代谢学
- 腎臟病學 (nephrology) 是一種醫學.
- 在医疗保健中的数据科学.
背景情况:
- 高尿路血是2型糖尿病 (T2DM) 的常见并发症.
- 它可以导致严重的健康问题,如痛风和脏疾病.
- 早期识别风险因素对于管理T2DM患者至关重要.
研究的目的:
- 开发和验证T2DM患者高尿素血风险的预测模型.
- 为了确定与此种人群中高尿路血症相关的关键临床和人口因素.
主要方法:
- 分析了8243名T2DM患者的队列.
- 机器学习技术,包括LASSO回归,用于变量选择.
- 一个人工神经网络 (ANN) 模型被开发和验证.
主要成果:
- 拉索回归确定了九个重要的变量,用于高尿路血症风险评估.
- 没有糖尿病药物治疗和血糖快速升高与风险有负相关性.
- 其他七个因素,包括肥胖和脂质概况,与高尿路血症风险有积极关联.
结论:
- 一个ANN模型有效地评估T2DM患者的高尿血症风险.
- 应特别注意女性,肥胖,血压,功能和脂质概况.
- 这些因素的积极管理可以减轻与并发性T2DM和高尿血症相关的风险.
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