Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Division of General Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
J Med Internet Res. 2024 Sep 4;26:e56022. doi: 10.2196/56022.
Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility.
This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence.
This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F-score.
The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement.
The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.
乳腺癌是一个全球性的主要健康问题，需要在复发预测和管理方面取得进展。使用 ChatGPT 开发人工智能（AI）临床决策支持系统（AI-CDSS）满足了这一需求，旨在提高预测准确性和用户可及性。
本研究旨在开发和验证一个基于网络的 AI-CDSS 应用程序的高级机器学习模型，利用 ChatGPT 的问答指导功能增强数据预处理和模型开发，从而提高乳腺癌复发预测的准确性。
本研究重点开发了一个高级机器学习模型，利用了来自三总医疗体系三军总医院乳腺癌登记处的 3577 名患者（2004-2016 年）的数据。作为一家三军总医院，它接受来自我国北部四个分支机构（3 个分支机构和 1 个离岛分支机构）的转诊，这些分支机构管理慢性病，但将复杂的手术病例（包括乳腺癌）转诊到主要中心，丰富了我们研究人群的多样性。模型训练使用了 2004 年至 2012 年的患者数据，随后使用 2013 年至 2016 年的数据进行验证，确保了预测模型的全面评估和稳健性。ChatGPT 是预处理和模型开发的重要组成部分，有助于激素受体分类、年龄分箱和独热编码。合成少数过采样技术等技术解决了数据集不平衡的问题。使用了各种算法，包括轻梯度提升机、梯度提升和极端梯度提升，并使用曲线下面积、准确性、敏感性和 F 分数等指标评估了它们的性能。
轻梯度提升机模型表现最佳，曲线下面积为 0.80，紧随其后的是梯度提升和极端梯度提升模型。AI-CDSS 工具的网络界面在临床决策场景中得到了有效测试，证明了它在个性化治疗计划和患者参与方面的用途。
由 ChatGPT 增强的 AI-CDSS 工具在乳腺癌复发预测方面取得了重大进展，为临床医生和患者提供了更个性化和可及的方法。虽然很有前景，但建议在不同的临床环境中进一步验证，以确认其疗效并扩大其应用。
