This study is based on weather information to predict drug consumption for four types of diseases that may be affected by the weather. We combine a 3D Convolution Neural Network (CNN) and a Long Short-Term Memory (LTSM) model to predict drug consumption of four types of diseases based on weather factors. In this system, 3D CNN is used for feature extraction, and LSTM is used to predict the following week's drug consumption. The study used a dataset that included 600,672 drug items with 1,318 different types, eliminating drug items without an ATC code. According to the ATC code, four types of diseases are cardiovascular system, dermatology, anti-infective system, and respiratory system, which are generally considered affected by weather factors. The proposed 3D CNN-LSTM model had a better performance than 2D CNN-LSTM, LSTM, four traditional models (ANN, KNN, SVR, and DT) and the current prediction method of the case hospital in the experiments. The prediction of drug consumption can help for prediction the need for drugs and solve some hospital logistics issues and inventory management.

CHAPTER I. INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Research Goal 2
CHAPTER II. LITERATURE REVIEWS 5
2.1 The Weather and Diseases 5
2.2 Classification of Drug Consumption Patterns 8
2.3 Micro-index of Drug Consumption 11
2.4 Weather Information 14
2.5 Algorithm 18
2.5.1 Convolutional Neural Network 19
2.5.2 Long Short Term Memory (LSTM) 20
2.5.3 Sliding Window scheme 22
CHAPTER III. METHODOLOGY 23
3.1 Data Collection 23
3.2 Data Processing 24
3.3 Experiment Design 24
CHAPTER IV. EXPERIMENT RESULTS ANALYSIS 31
4.1. Experiment Setting 31
4.2. Experiment Processing 31
4.3. Experiment Results 32
4.3.1. Experiment Results of Four-Week Training Data 32
4.3.1.1. Non-Sequential Training Data 32
4.3.1.2. Sequential Training Data 40
4.3.2. Experiment Results of Twelve-Week Training Data 48
4.3.2.1. Non-Sequential Training Data 48
4.3.2.2. Sequential Training Data 55
CHAPTER V. CONCLUSION 63
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