使用人工智能预测慢性病进展
Mario A Isaza-Ruget1, Nancy Yomayusa2, Camilo A González3
1Pathology and clinical laboratory. INPAC research group. Clinica Colsanitas. Keralty group, Fundación Universitaria Sanitas, Bogotá, Colombia.
BMC nephrology
|April 26, 2024
概括
机器学习模型预测慢性病 (CKD) 的进展和替代疗法 (RRT) 的需要. 一个时间到事件模型显示,在治疗慢性病患者的临床应用中具有前景.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 进展预测工具缺乏临床实用性.
- 开发高级CKD的准确预测模型至关重要.
研究的目的:
- 开发和验证用于预测CKD进展的机器学习模型.
- 预测3-5期慢性瘤患者需要替代疗法 (RRT) 的需要.
主要方法:
- 追溯性,观察性队列研究.
- 利用了CKD患者的人口,临床和实验室数据.
- 开发并比较了三种机器学习预测模型.
主要成果:
- 模型1 (4.5年风险) 的F1得分为0.82 (RRT),0.53 (阶段进展) 和0.55 (eGFR减少).
- 模型2 (时间到事件) 显示C指数为0.89 (RRT),0.67 (阶段进展) 和0.67 (eGFR减少).
- 模型3 (减少的模型2) 显示C指数为0.68 (RRT),0.68 (阶段进展) 和0.88 (eGFR减少).
结论:
- 时间到事件模型有效预测了5年内CKD进展结果.
- 这种模型可以帮助预测CKD患者的不良事件的发生和时间.
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