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Maedeh Karimian¹, Shahabedin Rahmatizadeh¹, Zeinab Kohzadi¹

¹Department of Anesthesiology, Anesthesiology Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran.

Anesthesiology and pain medicine

|March 13, 2025

概括

经常对麻醉师住院检查的模式分析揭示了关键的绩效趋势. 识别早期考试中的弱点可以改善国家委员会考试的成功和课程规划.

科学领域:

医学教育医学教育
麻醉学麻醉学
数据挖掘数据挖掘

背景情况:

住院是医疗专业人员获得专业知识的关键阶段.
麻醉学培训涉及严格的技能发展和知识获取.

研究的目的:

在麻醉学住院医生的每周检查中识别频繁的表现模式.
为了将这些模式与国家委员会考试结果相关联.

主要方法:

一项横截面研究分析了9个月的61名麻醉学住院医生 (CA-1至CA-4) 的每周检查数据.
使用阿普里奥里算法在考试成绩 (A-E) 中找到频繁的模式.
考试模式与国家考试结果进行了比较.

主要成果:

居民在第7次考试中获得平均分,在第1次和第5次考试中获得差分.
在培训考试 (ITE) 成绩和国家考试成绩之间发现了显著的相关性.
频繁的模式识别确定了居民弱点的特定领域.

结论:

频繁的模式分析有助于识别居民的优点和弱点.
洞察力可以为课程开发提供信息,并增强教育策略.
这种方法支持有针对性的干预措施,以改善居民的表现.

关键词:

麻醉学麻醉学雅普里奥里算法雅普里奥里算法人工智能的人工智能人工智能;麻醉学;实习和住院;阿普里奥里算法;数据挖掘;教育;医疗.数据挖掘数据挖掘教育教育教育教育教育教育.教育测量教育的测量实习和居住期间的实习和居住期间.

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