人工神经网络在预测发展中国家医生职业成就方面的解释性:适用性和影响
Dara Thomas1, Ying Li2, Chiagoziem C Ukwuoma3
1Business School, Sichuan University, Sichuan, China; Global Organization of African Academic Doctors (OAAD), P.O. Box 14833-00100, Langata, Nairobi, Kenya.
Social science & medicine (1982)
|September 19, 2024
概括
人工神经网络 (ANN) 模型准确地预测了发展中国家医生的职业成就. 这种人工智能工具通过识别关键的满意度和不满意度因素,有助于提高工作满意度和医疗保健质量.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 卫生政策和管理卫生政策和管理
背景情况:
- 职业成就对于医生的工作满意度和留任至关重要,特别是在发展中国家.
- 传统的职业指导对于复杂的职业成就挑战是不够的.
- 人工神经网络 (ANN) 模型为个性化的职业预测提供了潜力,但在适用性和解释性方面面临挑战.
研究的目的:
- 探索ANN模型的适用性,可解释性和影响,用于预测发展中国家医生职业生涯的成就.
- 确定影响职业成就的社会经济,心理和专业因素.
- 在这种情况下,评估ANN模型的性能和可解释性.
主要方法:
- 利用ANN模型预测尼日利亚和中国的医生职业成就.
- 员工可视化 (盒子图,热图) 和绩效指标 (MCC,泰勒图,MAE,MSE,RMSE,R2) 的使用情况.
- 分析特征重要性使用排列重要性,SHAP,LIME和威廉姆斯图.
主要成果:
- 在中国 (MSE=0.0004,R2=0.9994) 和尼日利亚 (MSE=0.0003,R2=0.9998) 的医生中,ANN模型表现出高的预测准确度.
- 确定了关键的满意度驱动因素:中国医生的IF1/IF2,尼日利亚医生的IF2/IF3.
- 确定了关键的不满预防因素:中国医生的EF1/EF3,尼日利亚医生的EF1/EF4.
结论:
- 在发展中国家,ANN模型是预测医生职业成就的有效工具.
- 这些发现支持使用人工智能来增强职业指导和明智决策.
- 这种方法可以有助于提高工作满意度,保持职位和整体医疗保健质量.
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