BACKGROUND/OBJECTIVESDespite the importance of a low-iodine diet (LID) for thyroid cancer patients preparing for radioactive iodine (RAI) therapy, few studies have evaluated dietary intake during LID. This study evaluated the amount of dietary iodine intake and its major food sources during a typical diet and during LID periods for thyroid cancer patients preparing for RAI therapy, and examined how the type of nutrition education of LID affects iodine intake.SUBJECTS/METHODSA total of 92 differentiated thyroid cancer patients with total thyroidectomy were enrolled from Seoul National University Hospital. All subjects completed three days of dietary records during usual and low-iodine diets before 131I administration.RESULTSThe median iodine intake was 290 µg/day on the usual diet and 63.2 µg/day on the LID. The major food groups during the usual diet were seaweed, salted vegetables, fish, milk, and dairy products and the consumption of these foods decreased significantly during LID. The mean energy intake on the LID was 1,325 kcal, which was 446 kcal lower than on the usual diet (1,771 kcal). By avoiding iodine, the intake of most other nutrients, including sodium, was significantly reduced during LID (P < 0.005). Regarding nutritional education, intensive education was more effective than a simple education at reducing iodine intake.CONCLUSIONIodine intake for thyroid cancer patients was significantly reduced during LID and was within the recommended amount. However, the intake of most other nutrients and calories was also reduced. Future studies are needed to develop a practical dietary protocol for a LID in Korean patients.
Accurate demand forecasting is crucial for industries to make strategic decisions and maintain their competitive edge. However, existing demand forecasting methods have prodigious problems, especially when it comes to handling the uncertainty, complexity, and nonlinearity of demand forecasting. In addition, the lack of historical data and data biases can create unreliable sources, which discourages the utilization of demand forecasting at a higher level of implementation in businesses. In addition, lack of historical data and data biases can create unreliable sources, which discourages utilization of demand forecasting at higher level of implementation in businesses. The proposed hybrid model aims to improve demand forecasting performance by combining the strengths of existing methods such as K-means clustering, LASSO regression, and LSTM deep learning. By leveraging these techniques, the model can overcome the limitations of each method and improve the accuracy of demand forecasting in various industries. K-means clustering helps to group similar data points, LASSO regression helps to select the most relevant features, and LSTM deep learning helps to capture the temporal dependencies in the data. The combination of these techniques can result in a more accurate and robust demand forecasting model. The model was tested on 2,548 retail products, and outperformed three benchmarking models using the mMAPE, RMSE, and MAE indicators. The proposed model can be used in the retail industry to improve management performance and decision-making, and its ability to optimize variables for each cluster can improve resource allocation.
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