基于人工智能的治疗失败和药物不服药的预测
Mert Başaranoğlu1, İsa Kamil Taşdemir2, Erdem Akbay2
1Department of Urology, Faculty of Medicine, Mersin University, Ciftlikkoy Campus, Yenişehir, Mersin, 33343, Turkey. mertbasaranoglu@gmail.com.
BMC urology
|August 21, 2025
概括
一个人工智能模型准确地预测了过度活跃的膀治疗失败和不坚持,提前识别高风险患者以获得更好的结果.
科学领域:
- 尿道病学
- 医学的人工智能
- 预测分析
背景情况:
- 由于治疗失败和药物不服药,膀过活的管理面临挑战.
- 早期预测这些问题对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和验证基于人工智能的预测模型,用于早期识别OAB患者的治疗失败和不服药.
- 专注于包括糖尿病神经病变在内的多种病理亚组.
主要方法:
- 对285名OAB患者的追溯分析 (2018年1月至2025年4月).
- 人工智能模型使用超声波,尿液流量测量,空白日记和问卷进行开发.
- 主要结局:治疗失败和三个月后不服药.
主要成果:
- 渐变增强模型在治疗失败时达到87.3%的准确率,在不坚持治疗时达到85.1%.
- 主要预测因素:膀壁厚度,糖尿病持续时间>7年,急性改善<25%.
- 糖尿病神经病症小组的治疗失败率最高 (42. 8%).
结论:
- 人工智能模型有效地识别了OAB患者的治疗失败和不坚持风险.
- 允许早期干预和治疗修改以改善结果.
- 提高医疗保健资源利用的潜力.
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