目的 探讨结肠镜下息肉切除术后复发的危险因素，并基于机器学习算法构建复发风险预警模型，为防治对策提供依据。方法 回顾性收集2018年9月—2023年9月六安市人民医院1 058例初次行无痛结肠镜下息肉切除术患者的临床资料，使用单因素和多因素Logistic回归分析筛选复发危险因素。采用7∶3随机抽样分为训练集和验证集，分别通过决策树、贝叶斯及Logistic回归算法构建预测模型，并以受试者工作特征曲线（ROC）曲线下面积（AUC）、灵敏度、特异度等指标来评估模型效能。结果 单因素分析显示，性别、吸烟、代谢综合征、息肉数量、息肉位置、山田分型、组织病理学类型、切除方式、复查时间、肠息肉直径、手术时间是复发的危险因素（P＜0.05）。多因素分析显示，性别、代谢综合征、息肉数量、息肉直径、肠息肉位置、山田分型、组织学病理类型、切除方式、手术时间均是结肠息肉内镜下切除术后复发的危险因素。模型评估显示，决策树算法、贝叶斯算法、Logistic回归算法的ROC曲线下面积（AUC）分别为0.849、0.818、0.811；灵敏度分别为85.14%、81.62%、79.43%；特异度分别为81.69%、79.45%、74.18%；约登指数分别为0.534、0.551、0.573；95%CI分别为0.810~0.876、0.794~0.860、0.782~0.850；决策树算法模型效能最佳，Logistic回归算法的性能最差。结论 性别、代谢综合征、肠息肉特征（数量、直径、位置等）是术后复发的关键危险因素。决策树模型在风险预测中表现最优，可为临床制定个体化随访策略提供参考。
Objective To explore the risk factors for recurrence after painless colonoscopic polypectomy and construct a recurrence risk warning model based on machine learning algorithms to provide evidence for prevention and treatment strategies．Methods A retrospective analysis was conducted on clinical data from 1 058 patients who underwent their first painless colonoscopy-guided polypectomy at our hospital between September 2018 and September 2023．Univariate and multivariate Logistic regression analyses were performed to identify recurrence risk factors．The dataset was randomly divided into training and validation sets using a 7∶3 ratio．Prediction models were constructed using decision tree，Bayesian，and Logistic regression algorithms，and their performance was evaluated using metrics such as the area under the receiver operating characteristic curve（AUC），sensitivity，specificity，and others．Results Univariate analysis revealed that gender，smoking，metabolic syndrome，number of polyps，polyp location，Yamada classification，histopathological type，resection method，follow-up time，polyp diameter，and operation duration were risk factors for recurrence（P＜0.05）．Multivariate analysis identified gender，metabolic syndrome，number of polyps，polyp diameter，polyp location，Yamada classification，histopathological type，resection method，and operation duration as independent risk factors for recurrence after endoscopic polypectomy．Model evaluation showed AUC values of 0.849，0.818，and 0.811 for the decision tree，Bayesian，and Logistic regression algorithms，respectively．Sensitivity values were 85.14%，81.62%，and 79.43%；specificity values were 81.69%，79.45%，and 74.18%；Youden’s indices were 0.534，0.551，and 0.573；and 95% confidence intervals（CIs）were 0.810–0.876，0.794–0.860，and 0.782–0.850，respectively．The decision tree algorithm demonstrated the best predictive performance，while the Logistic regression algorithm performed the least favorably．Conclusions Gender，metabolic syndrome，and polyp characteristics（number，diameter，location，etc．）are key risk factors for recurrence after polypectomy．The decision tree algorithm exhibited optimal predictive efficacy，offering valuable insights for developing individualized follow-up strategies in clinical practice．
目的 通过机器学习方法构建脓毒症谵妄患者30 d死亡的预测模型，并识别关键预测因子。方法 采用基于医疗信息集成重症监护数据库（Medical Information Mart for Intensive Care IV）的回顾性队列研究方法，boruta筛选重要特征，并通过决策树，K近邻，LightGBM，随机森林，支持向量机，XGBoost构建模型进行分析，通过ROC曲线下面积进行评估，利用F1分数、召回率、精确率、特异度、灵敏度和阳性预测值比较模型表现。结果 XGBoost模型在训练集和验证集中的ROC曲线下面积分别为0.906和0.762，表明该模型具有良好的预测能力，入院年龄、红细胞分布宽度和白细胞计数是最重要的预测因子。结论 基于机器学习的脓毒症谵妄患者预后预测模型展现出良好的预测效能，为临床早期干预提供了重要参考依据。
Objective To construct a 30-day mortality prediction model for patients with sepsis-associated delirium using machine learning methods and identify key predictive factors．Methods A retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care IV database．Important features were selected using the Boruta algorithm，and models including Decision Tree，K-Nearest Neighbors，LightGBM，Random Forest，Support Vector Machine，and XGBoost were constructed and analyzed．Model performance was evaluated using the area under the reciver operater characteristic（ROC）curve（AUC），along with F1 score，recall，precision，specificity，sensitivity，and positive predictive value．Results The XGBoost model demonstrated strong predictive performance，with AUC values of 0.906 in the training set and 0.762 in the test set．Key predictors identified included admission age，red blood cell distribution width，and white blood cell count．Conclusions The machine learning-based prediction model for sepsis-associated delirium prognosis exhibits robust predictive efficacy，providing a valuable tool for early clinical intervention．
作为中医药文化瑰宝，针灸在疾病治疗中展现出独特价值，但其标准化操作、疗效量化评价及安全风险控制仍是制约现代化转型的关键问题。随着人工智能技术的突破性发展，机器学习在医疗领域引发的技术革命为针灸创新提供了新机遇。超声医学凭借可视化、定量化及动态监测优势，与机器学习算法形成技术协同，推动传统针灸向精准化、智能化方向迈进。通过超声成像实时定位穴位层次结构与针体轨迹，结合机器学习对多维数据的深度分析，显著提升了针刺治疗的精准性与安全性，同时为建立客观疗效评价体系提供了技术支撑。文章旨在全面回顾超声引导下机器学习技术在针灸研究领域的应用状况，深入剖析现有研究中存在的挑战与局限，并前瞻性地探讨未来的研究方向与趋势，旨在为针灸实践与应用研究的深化与发展提供理论指导与实践启示。
As an invaluable component of traditional Chinese medicine，acupuncture boasts a distinctive value in thetreatment of diseases．However，the standardization of its practice，the quantitative assessment of its therapeutic efficacy，and the implementation of safety risk control measures remain pivotal challenges hindering its modernization and transformation．The advent of artificial intelligence technology has precipitated a technological revolution in the medical field，thereby generating novel opportunities for innovation in acupuncture．The integration of ultrasound medicine and machine learning algorithms，leveraging their respective strengths in visualization，quantification and dynamic monitoring，has emerged as a technological synergy．This synergy is poised to propel traditional acupuncture towards precision and intelligence．The integration of ultrasound imaging with machine learning algorithms enables real-time localization of acupoints and needle trajectory，enhancing the precision and safety of acupuncture treatment．Additionally，it facilitates the development of objective efficacy evaluation systems．The present article aims to comprehensively review the application of ultrasound-guided machine learning technology in the field of acupuncture and moxibustion research．It does so by first analyzing the challenges and limitations of existing research and then prospectively exploring future research directions and trends．The article’s ultimate aim is to provide theoretical guidance and practical inspiration for the deepening and development of acupuncture and moxibustion practice．
近年来，人工智能技术（AI）的发展正在逐渐改变传统的医疗行业，机器学习作为人工智能技术中的主流被越来越多地应用于分析复杂的医学数据，为疾病的诊断、预后风险评估、诊疗决策的制定等方面提供了便利。文章对国内外机器学习算法在术后谵妄中的应用进行综述，以期为术后谵妄预测模型的构建提供新的思路，为临床早期评估术后谵妄提供新的依据。
In recent years，the development of artificial intelligence（AI）is gradually changing the traditional medical industry．Machine learning，as the mainstream of artificial intelligence technology，is increasingly applied to analyze complex data in medical research．It provides convenience for disease diagnosis，risk assessment and diagnosis and treatment decision making．This paper reviews the application of machine learning algorithms in postoperative delirium at home and abroad，in order to provide a new idea for the construction of postoperative delirium prediction model and a new basis for early clinical evaluation of postoperative delirium．
