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- W2994183887 abstract "Efficient drug distribution, including maintaining the right inventory levels and purchasing the right supplies at the regional level, is a major policy concern in Brazil. This work examines the use of statistical models and deep learning methods to forecast quarterly drug distribution across the different states in Brazil. Using monthly univariate time-series of hundreds of drugs, the applied predictors consistently provided a smaller mean absolute error than the model currently in use by the Brazilian Ministry of Health (MS). Of the different models used, an LSTM seq2seq model provided the best performance most frequently. Using the best model for each case could mitigate drug shortage and significantly reduce the amount of resources used for drug distribution, saving millions of dollars for the Government." @default.
- W2994183887 created "2019-12-13" @default.
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- W2994183887 date "2019-10-01" @default.
- W2994183887 modified "2023-09-27" @default.
- W2994183887 title "Statistical and Deep Learning Models for Forecasting Drug Distribution in the Brazilian Public Health System" @default.
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- W2994183887 doi "https://doi.org/10.1109/bracis.2019.00130" @default.
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