首页|Shandong University Reports Findings in Lung Cancer (Exploring pollutant joint effects in disease through interpretable machine learning)
Shandong University Reports Findings in Lung Cancer (Exploring pollutant joint effects in disease through interpretable machine learning)
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2024 FEB 27 (NewsRx) – 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 reporting from Qingdao, People’s Republic of China, by NewsRx journalists, research stated, “Identifying the impact of pollutants on diseases is crucial. However, assessing the health risks posed by the interplay of multiple pollutants is challenging.” The news correspondents obtained a quote from the research from Shandong University, “This study introduces the concept of Pollutants Outcome Disease, integrating multidisciplinary knowledge and em- ploying explainable artificial intelligence (AI) to explore the joint effects of industrial pollutants on diseases. Using lung cancer as a representative case study, an extreme gradient boosting predictive model that in- tegrates meteorological, socio-economic, pollutants, and lung cancer statistical data is developed. The joint effects of industrial pollutants on lung cancer are identified and analyzed by employing the SHAP (Shapley Additive exPlanations) interpretable machine learning technique. Results reveal substantial spa- tial heterogeneity in emissions from CPG and ILC, highlighting pronounced nonlinear relationships among variables. The model yielded strong predictions (an R of 0.954, an RMSE of 4283, and an R of 0.911) and emphasized the impact of pollutant emission amounts on lung cancer responses. Diverse joint effects patterns were observed, varying in terms of patterns, regions (frequency), and the extent of antagonistic and synergistic effects among pollutants.”
QingdaoPeople’s Republic of ChinaAsiaCancerCyborgsEmerging TechnologiesHealth and MedicineLung CancerLung Diseases and ConditionsLung Neo- plasmsMachine LearningOncologyRisk and Prevention
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