导向:用于口腔健康预测和研究的自动机器学习分类器
Inmaculada Gomez-Rios1, Esteban Egea-Lopez2, Antonio José Ortiz Ruiz1
1Department of Dermatology, Stomatology, Radiology and Physical Medicine, Universidad de Murcia, Murcia, Spain.
BMC oral health
|June 20, 2023
概括
本研究介绍了ORIENTATE,这是一款软件,使临床医生能够在没有技术专业知识的情况下使用机器学习进行医疗预测. 它成功地预测了儿童需要第二次镇静的需要,并确定了关键的预测因素.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床决策支持系统 临床决策支持系统
- 机器学习在医学中的应用
背景情况:
- 数据驱动的方法在医疗保健中至关重要,但其采用受到技能差距的限制.
- 临床从业者往往缺乏开发和解释机器学习模型的专业知识.
- 导向软件通过自动化机器学习分类来解决这个差距.
研究的目的:
- 在临床实践中引入ORIENTATE,这是一款用于自动机器学习分类的用户友好的软件.
- 让没有特定技术技能的临床从业者能够构建和解释预测模型.
- 为了促进统计推断,并使用机器学习输出来补充经典研究.
主要方法:
- 导向自动化功能选择和目标变量识别.
- 该软件生成,交叉验证和评估多个分类模型.
- 它包括一个自定义的功能选择算法,用于系统预测器搜索.
- 用全球解释方法和预测接口生成了全面的报告.
主要成果:
- ORIENTATE被应用于对接受深度镇静的儿童的数据集.
- 该软件实现了对第二次镇静需要的高预测准确度 (F1分数为0.83,ROC AUC为0.92).
- 确定了8个预测因素,首次镇静时需要脉治疗的牙数量是关键预测因素.
结论:
- 导向使研究人员和临床医生能够利用机器学习进行预测和推断任务.
- 该软件有助于在预防性医疗保健中应用机器学习分类.
- 它作为传统统计研究的宝贵补充,增强数据解释.
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