Developing Intelligent and Immutable Vaccine Supply and Operation Platform Using Blockchain and Artificial Intelligence Technologies

Abstract

During times of crisis, like in healthcare emergencies or disruptions in logistics and supply chains, decision-makers encounter significant challenges in managing resources efficiently. This study proposes an approach combining different information types and advanced computer techniques to improve how we handle crises. By looking at data connected with blockchain technology, we aim to understand crises better and help people make smarter decisions. We use a special kind of computer model called Long Short-Term Memory (LSTM) to guess what might happen during a crisis, like how many people might get sick or if there might be problems with getting supplies. Our model is right about 87% of the time. To understand why the computer makes these guesses, we use a method called SHapley Additive exPlanations (SHAP), which is widely known. However, we notice that SHAP sometimes doesn’t explain things well for the entire period we’re looking at. So, we suggest some changes to the SHAP method to explain things better for smaller parts of time. These changes help the method explain things better for small bits of time, but it might not be as good at explaining everything overall. To ensure our idea works, we compare our computer model’s guesses and explanations with those from other models that look at data differently. We show the good and bad parts of our method in different crises.

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