首页|University of Electronic Science and Technology of China Reports Findings in Lym phoma (Development and validation of machine learning models for predicting canc er-related fatigue in lymphoma survivors)
University of Electronic Science and Technology of China Reports Findings in Lym phoma (Development and validation of machine learning models for predicting canc er-related fatigue in lymphoma survivors)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Lymphoma is the subject of a report. According to news reporting originating in Chengdu, Pe ople’s Republic of China, by NewsRx journalists, research stated, “New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical di agnosis and treatment of CRF are inadequate and require enhancement.” The news reporters obtained a quote from the research from the University of Ele ctronic Science and Technology of China, “The main objective of this study is to construct machine learning-based CRF prediction models for lymphoma survivors t o help healthcare professionals accurately identify the CRF population and bette r personalize treatment and care for patients. A cross-sectional study in China recruited lymphoma patients from June 2023 to March 2024, dividing them into two datasets for model construction and external validation. Six machine learning a lgorithms were used in this study: Logistic Regression (LR), Random Forest, Sing le Hidden Layer Neural Network, Support Vector Machine, eXtreme Gradient Boostin g, and Light Gradient Boosting Machine (LightGBM). Performance metrics like the area under the receiver operating characteristic (AUROC) and calibration curves were compared. The clinical applicability was assessed by decision curve, and Sh apley additive explanations was employed to explain variable significance. CRF i ncidence was 40.7 % (dataset I) and 44.8 % (dataset II). LightGBM showed strong performance in training and internal validation. LR excelled in external validation with the highest AUROC and best calibration. Pai n, total protein, physical function, and sleep disturbance were important predic tors of CRF.”
ChengduPeople’s Republic of ChinaAsi aCancerCyborgsEmerging TechnologiesHealth and MedicineHematologyImmu noproliferative DisordersLymphatic Diseases and ConditionsLymphomaLymphopr oliferative DisordersMachine LearningOncology
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