This study aimed to predict stress in patients using artificial intelligence (AI) from biological signals and verify the effect of stress on respiratory irregularity. We measured 123 cases in 41 patients and calculated stress scores with seven stress-related features derived from heart-rate variability. The distribution and trends of stress scores across the treatment period were analyzed. Before-treatment information was used to predict the stress features during treatment. AI models included both non-pretrained (decision tree, random forest, support vector machine, long short-term memory (LSTM), and transformer) and pretrained (ChatGPT) models. Performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. Respiratory irregularities were calculated in phase and amplitude and analyzed for correlation with stress score. Over 90% of the patients experienced stress during radiation therapy. LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659; stress classification, LSTM: 0.846, GPT4.0: 0.769). A 10% increase in stress score was associated with a 0.286 higher phase irregularity (p< 0.025). Our research pioneers the use of AI and biological signals for stress prediction in patients undergoing radiation therapy, potentially identifying those needing psychological support and suggesting methods to improve radiotherapy effectiveness through stress management.
本研究旨在利用人工智能（AI）通过生物信号预测患者压力，并验证压力对呼吸不规律性的影响。我们对41名患者的123例数据进行了测量，基于心率变异性提取的七个压力相关特征计算了压力评分，并分析了治疗期间压力评分的分布与变化趋势。利用治疗前信息预测治疗期间的压力特征，采用的AI模型包括未经预训练的模型（决策树、随机森林、支持向量机、长短期记忆网络（LSTM）和Transformer）以及经过预训练的模型（ChatGPT）。通过10折交叉验证、精确匹配率、准确率、召回率、精确度和F1分数对模型性能进行评估。同时，计算了呼吸的相位和幅度不规律性，并分析其与压力评分的相关性。结果显示，超过90%的患者在放射治疗期间经历了压力。LSTM和经过提示工程优化的GPT4.0模型表现出最高的预测准确率（特征分类：LSTM为0.703，GPT4.0为0.659；压力分类：LSTM为0.846，GPT4.0为0.769）。压力评分每增加10%，相位不规律性相应增加0.286（p<0.025）。本研究开创性地将AI与生物信号结合用于预测放射治疗患者的压力状态，有望识别需要心理支持的患者，并通过压力管理为提高放疗效果提供方法参考。
肿瘤(癌症)患者之家