首页|Department of Industrial Design Reports Findings in Lung Cancer (Development and comparison of machine-learning models for predicting prolonged postoperative le ngth of stay in lung cancer patients following video-assisted thoracoscopic surg ery)
Department of Industrial Design Reports Findings in Lung Cancer (Development and comparison of machine-learning models for predicting prolonged postoperative le ngth of stay in lung cancer patients following video-assisted thoracoscopic surg ery)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Lung Cancer is the subject of a report. According to news originating from Hangzhou, People ’s Republic of China, by NewsRx correspondents, research stated, “This study aim ed to develop models for predicting prolonged postoperative length of stay (PPOL OS) in lung cancer patients undergoing video-assisted thoracoscopic surgery (VAT S) by utilizing machine-learning techniques. These models aim to offer valuable insights for clinical decision-making.” Our news journalists obtained a quote from the research from the Department of I ndustrial Design, “This retrospective cohort study analyzed a dataset of lung ca ncer patients who underwent VATS, identifying 25 numerical features and 45 textu al features. Three classification machine-learning models were developed: XGBoos t, random forest, and neural network. The performance of these models was evalua ted based on accuracy (ACC) and area under the receiver operating characteristic curve, whereas the importance of variables was assessed using the feature impor tance parameter from the random forest model. Of the 6767 lung cancer patients, 1481 patients (21.9%) experienced a postoperative length of stay of > 4 days. The majority were male (4111, 60.8% ), married (6246, 92.3%), and diagnosed with adenocarcinoma (4145, 61.3%). The Random Forest classifier exhibited superior prediction performance with an area under the curve (AUC) of 0.792 and ACC of 0.804. The ca libration plot revealed that all three classifiers were in close alignment with the ideal calibration line, indicating high calibration reliability. The five mo st critical features identified were the following: surgical duration (0.116), a ge (0.066), creatinine (0.062), hemoglobin (0.058), and total protein (0.054). T his study developed and evaluated three machine-learning models for predicting P POLOS in lung cancer patients undergoing VATS. The findings revealed that the Ra ndom Forest model is most accurately predicting the PPOLOS.”
HangzhouPeople’s Republic of ChinaAs iaCancerCyborgsEmerging TechnologiesHealth and MedicineLung CancerLu ng Diseases and ConditionsLung NeoplasmsMachine LearningOncologySurgeryThoracic Surgical ProceduresThoracoscopy
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