一个先进的机器学习模型用于基于Web的基于人工智能的临床决策支持系统应用:模型开发和验证研究
Tai-Han Lin1, Hsing-Yi Chung1, Ming-Jr Jian1
1Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
这项研究开发了一个使用ChatGPT预测乳腺癌复发的AI-CDSS,在光梯度增强机器模型中达到0.80的AUC. 该工具增强了个性化治疗规划和患者参与度.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 预测乳腺癌复发是瘤学中的一个关键挑战.
- 人工智能临床决策支持系统 (AI-CDSS) 提供了提高预测准确性和可访问性的潜力.
- 聊天GPT集成旨在提高AI-CDSS的开发和可用性.
研究的目的:
- 为基于Web的AI-CDSS开发和验证一个先进的机器学习模型.
- 利用ChatGPT进行改进的数据预处理和模型开发,以预测乳腺癌复发.
- 提高乳腺癌复发预测工具的准确性和可访问性.
主要方法:
- 利用了来自Tri-Service总医院的3577名乳腺癌患者 (2004-2016) 的数据集.
- 采用ChatGPT进行数据预处理任务,包括分类,分类和编码.
- 经过训练和验证的模型使用算法,如光梯度增强机,梯度增强和极端梯度增强,以AUC,精度,灵敏度和F1评分来评估性能.
主要成果:
- 增强光度梯度的机器模型实现了最高的性能,曲线下的面积 (AUC) 为0.80.
- 梯度增强和极端梯度增强模型也显示出强大的预测能力.
- 人工智能-CDSS网络接口在个性化治疗规划的临床场景中表现出有效性.
结论:
- 由ChatGPT增强的AI-CDSS在预测乳腺癌复发方面取得了重大进展.
- 该系统为临床医生和患者提供了更加个性化和可访问的方法.
- 建议在不同的临床环境中进一步验证,以确认疗效和更广泛的应用.
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