Muhtasim Hafiz
ABSTRACT
Health services are one of the necessities for a human being. Good quality and timely health services are essential for proper health conditions of human requirement. Distribution of health care facilities and services are imperative in any nation thus anticipating demand and taking pre-emptive decision to adjust the supply for the future is essential. A responsive and synchronised flow of the products is necessary. The aim of this paper is to present the forecasting model and predicted medicine demand in all district of Bangladesh.
- A. Molina, B. Ponte, J. Parreño, D. De la Fuente, J. Costas "Forecasting erratic demand of medicines in a public hospital: A comparison of artificial neural networks and ARIMA models" Int'l Conf. Artificial Intelligence 2016Google Scholar
- Gökçe Candan, Mehmet Fatih Taşkin, Harun Reşit Yazgan "Demand Forecasting in Pharmaceutical Industry Using Artificial Intelligence: Neuro-Fuzzy Approach" Journal of Military and Information Science 2014Google Scholar
- Sanders, N. R., & Manrodt, K. B. (2003). The efficacy of using judgmental versus quantitative forecasting methods in practice. Omega, 31(6), 511--522. https://doi.org/10.1016/j.omega.2003.08.007Google ScholarCross Ref
- Lee, C. K., Song, H. J., & Mjelde, J. W. (2008). The forecasting of International Expo tourism using quantitative and qualitative techniques. Tourism Management, 29(6), 1084--1098. https://doi.org/10.1016/j.tourman.2008.02.007Google ScholarCross Ref
- S. Lakshmi Anusha, Swati Alok, Ashiff Shaik "Demand Forecasting for the Indian Pharmaceutical Retail: A Case Study" Journal of Supply Chain Management Systems April 2014Google Scholar
- Angeliki Papana, Dimitris Folinas & Anestis Fotiadis "Forecasting the consumption and the purchase of a drug" 2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS 2012Google Scholar
- Brown, R.G. Exponential Smoothing for Predicting Demand. Cambridge, Massachusetts: Arthur D. Little Inc. pp. 15, 1956Google Scholar
- Huang C. and H. Yang. A Time Series Approach to Short Term Load Forecasting through Evolutionary Programming Structures. Proceedings of the International Conference on Energy Management and Power Delivery (EMPD'95), Vol. 2, 583--588, 1995.Google ScholarCross Ref
- Box G.E.P. and G. Jenkins. Time Series Analysis: Forecasting and Control. HoldenDay, 1976Google Scholar
- Björn Wettermark, Marie E Parsonz, Nils Wilking, Mats Kalin, Seher Korkmaz, Paul Hjemdahl, Brian Godman, Max Petzold, Lars L Gustafsson "Forecasting drug utilization and expenditure in a metropolitan health region" BMC Health Services Research 2010Google Scholar
- Shumway R, Stoffer D (2011). Time Series Analysis and its applications. London: SpringerGoogle Scholar
- Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3 PART 2), 6697--6707. https://doi.org/10.1016/j.eswa.2008.08.058Google ScholarDigital Library
- Yue, L., Yafeng, Y., Junjun, G., & Chongli, T. (2007). Demand forecasting by using support vector machine. Proceedings - Third International Conference on Natural Computation, ICNC 2007, 3(Icnc), 272--276. https://doi.org/10.1109/ICNC.2007.324Google ScholarDigital Library
- Diederik P. Kingma, Jimmy Ba "Adam: A Method for Stochastic Optimization"https://github.com/jinglescode/demos/tree/master/src/app/components/tfjs-timeseries-stocks?source=postGoogle Scholar
- Varsamopoulos, S & Bertels, Koen & G. Almudever, Carmen. (2018). Designing neural network based decoders for surface codes.Google Scholar
- Tax N., Verenich I., La Rosa M., Dumas M. (2017) Predictive Business Process Monitoring with LSTM Neural Networks. In: Dubois E., Pohl K. (eds) Advanced Information Systems Engineering. CAiSE 2017. Lecture Notes in Computer Science, vol 10253. Springer, ChamGoogle ScholarCross Ref
- Yu Wang "A new concept using LSTM Neural Networks for dynamic system identification" May 2017, IEEE;Google Scholar
- Sepp Hochreiter, Jürgen Schmidhuber "LONG SHORT-TERM MEMORY" Neural Computation 9(8):1735--1780, 1997Google Scholar
- Yu Wang "A new concept using LSTM Neural Networks for dynamic system identification" American Control Conference (ACC) 2017Google Scholar
- Chang Gao, Daniel Neil, Enea Ceolini, Shih-Chii Liu, Tobi Delbruck "DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator" Monterey, CALIFORNIA, USA --- February 25 - 27, 2018Google Scholar
Index Terms
- Predicting the Demand of Prescribed Medicines in Bangladesh using Artificial Intelligent (AI) based Long Short-Term Memory (LSTM) Model
Recommendations
Machine learning-based demand forecasting in cancer palliative care home hospitalization
Graphical abstractDisplay Omitted
Highlights- Demand forecasting is considerably challenging and extremely important in palliative home care and home hospitalization where we have to manage sharp demand ...
Abstract ObjectiveTo develop an effective Management Information System (MIS) that is empowered by predictive models that can forecast the demand of end-stage cancer home hospitalized patients in individual and population levels, ...
A fully adaptive forecasting model for short-term drinking water demand
For the optimal control of a water supply system, a short-term water demand forecast is necessary. We developed a model that forecasts the water demand for the next 48 h with 15-min time steps. The model uses measured water demands and static calendar ...
Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources
AbstractThe influenza problem has always been an important global issue. It not only affects people’s health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control ...
Comments