Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology
本文基于物联网优势，重点研究癌症患者康复智能推荐模型，设计了一种人性化的癌症康复方案智能推荐系统。针对癌症患者复发原因和时间的不确定性，采用卷积神经网络算法对两者进行预测。模型预测结果显示，预测准确率较高，达到92%。
D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer
本文收集了肝癌、肺癌、肾癌等7例癌症患者的50,000例样本。提出了一种基于DNN加权的TSVR算法。本文采用改进的TSVR算法建立了癌症复发预测模型。该模型对各种癌症的预测准确率可达91%以上。
Patient-Reported Outcome-Based Symptom Management Versus Usual Care After Lung Cancer Surgery
本研究旨在评估肺癌术后早期基于患者报告结局（PRO）的症状管理的有效性与可行性。研究结果显示，干预组并发症发生率（21.5%）显著低于对照组（40.6%，P=0.019）。
An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning
本文提出了一种基于联邦学习的癌症患者辅助诊断模型。CNN的联邦预测模型在联合建模和仿真条件下对5类癌症数据的准确率达到90%以上，准确率优于单一建模机器学习树模型和线性模型。
Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost
利用乳腺癌患者术后康复资料，构建了以患者指标为输入，以复发位置为输出的乳腺癌康复智能预测模型。该模型的预测准确率为>93%，可以有效预测乳腺癌的复发位置。
A random forest-neural network coupled model for predicting the recurrence location of uterine cancer
在这项研究中，我们建立了一个预测模型，通过结合随机森林和神经网络算法来确定子宫癌复发的位置。与传统方法相比，该模型在预测子宫癌复发位置方面更有效，预测准确率高达88.63%。
Review on security of federated learning and its application in healthcare
本文梳理了近期关于联邦学习和医疗行业应用的论文，概述了联邦学习的几种架构和分类模型，研究了联邦学习在医疗保健中的应用，并分析了联邦学习和医疗应用程序的安全性。
An Enhanced Multi-Scale Collaborative Learning Network for Medical Image Segmentation in Internet of Medical Things
本文提出了一种多尺度协作学习网络（MSCL-Net）结合医疗物联网（IoMT）平台，旨在提高腹部器官分割的准确性。实验结果表明，MSCL-Net优于现有的先进算法。
参考文献列表：
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