PDF(Pubmed)
Abstract：
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.
摘要：
本研究旨在使用人工智能（AI）从生物信号中预测患者的压力，并验证压力对呼吸不规则的影响。我们测量了41例患者的123例病例，并计算了来自心率变异性的七个压力相关特征的压力评分。分析整个治疗期间应激评分的分布和趋势。治疗前信息用于预测治疗期间的应激特征。人工智能模型既包括非预训练的(决策树，随机森林,支持向量机，长短期记忆(LSTM)，和变压器)和预训练(ChatGPT)模型。使用10倍交叉验证评估性能，精确匹配比，准确度,召回，精度,F1得分。计算呼吸不规则的相位和幅度，并分析其与压力评分的相关性。超过90％的患者在放射治疗期间经历了压力。LSTM和提示工程GPT4.0具有最高的精度(特征分类，LSTM：0.703，GPT4.0：0.659；应力分类，LSTM：0.846，GPT4.0：0.769）。应力评分增加10%与0.286更高的相位不规则性相关(p<0.025)。我们的研究开创了人工智能和生物信号用于放射治疗患者压力预测的先河，潜在识别需要心理支持的患者,并提出通过压力管理提高放疗效果的方法.
