Estimating individual risk for lung cancer.
Lung cancer risk prediction models hold the promise of improving patient care and streamlining research. The ultimate goal of these models is to inform clinicians as to which interventions their individual patients should receive to reduce lung cancer-associated morbidity and mortality. In this paper, we discuss the history and current state of lung cancer prediction models, focusing on three models: the Bach model, the Spitz model, and the Liverpool Lung Project (LLP) model. We also discuss the prospects for further development of improved prediction models for lung cancer risk. Although current models can identify those smokers at highest risk for lung cancer, these models are presently of limited use in the clinical setting. Nevertheless, lung cancer risk prediction models can be used during study enrollment to select more appropriate study subjects, and may eventually be useful in identifying patients for lung cancer screening or to receive chemoprevention.
Abstract Forecasting of the market demand is an important part of supply chain management of the tobacco wholesalers in China. Traditional forecasting method for tobacco market demand bases on the orders of retailers and the evaluations of the different departments which use different forecasting methods. The existing forecasting method causes normally large forecasting errors between the forecasting results and the actual sales amount. This paper presents a new market forecasting system for tobacco wholesalers in China based on a new developed market demand forecasting model. By applying the new system for a sales company in China shows that this model can help sales company to improve the forecasting accuracy for annual and monthly market demands significantly.