Hospital medicine purchase plan prediction method and system based on LSTM

Abstract

The invention discloses a hospital medicine purchase plan prediction system based on LSTM. The system comprises an AI server (1), a big data storage module (2), a purchase management system interaction end (3), an LSTM module (4), a medicine use acquisition interface (5) and a medicine off-shelf management module (10), wherein the AI server (1) is in data communication connection with the big data storage module (2), the purchase management system interaction end (3) and the medicine use acquisition interface (5) through a data communication network, the AI server (1) is further in data communication connection with the LSTM module (4), and the LSTM module (4) is further in data communication connection with the big data storage module (2). According to the invention, the LSTM method is adopted to process and analyze the big data, and the data abnormal points are eliminated, so that the purchasing of hospital medicines is more accurate and is not affected by the abnormal data. The abnormal data is analyzed to determine whether sudden diseases are found, so that the overall health of a city is better maintained, and sudden diseases and infectious diseases are retrieved.

Description

technical field [0001] The invention relates to the technical field of drug procurement management, in particular to a method and system for predicting hospital drug procurement plans based on LSTM. Background technique [0002] Hospitals are an important guarantee for people to keep healthy, and an important place to solve patients' diseases and eliminate pain. To maintain normal operations, hospitals must provide sufficient quantities of safe and effective pharmaceutical products. In order to maintain sufficient data and safe and effective drugs, it is necessary to purchase enough drugs in a timely manner. However, for the use of drugs, due to differences in different periods, diseases, and the number of people who see the doctor, the amount of drugs used will vary. Too much will lead to expired medicines, which cannot be used, so they can only be wasted, which does not meet the economic requirements; but if the purchase of medicines is small, it will lead to a shortage of...

AI Technical Summary

Problems solved by technology

[0011] 1. Traditionally, in the hospital’s procurement of drugs, the supplement of drugs is only considered based on the remaining drug inventory in the hospital pharmacy, but the validity period is not considered. The validity period is only checked by personnel, which may lead to insufficient drugs. But the substance has expired and cannot be used

[0012] 2. In the prior art, usually different types of diseases are also related to seasons, such as rheumatism, respiratory diseases, etc. are related to seasons, and the arrival time of seasonal changes in different years is different. Therefore, the amount and type of drugs used in hospitals Different, this will inevitably affect the procurement of medicines, but this point has not been noticed in the prior art, and it is easy to cause medicines to expire and be wasted

[0013] 3. In the existing technology, since hospitals cover a certain area and a certain range of people for disease treatment, but the characteristics of the population structure and the population size also determine the characteristics of the population's disease and the drugs used for the corresponding disease, so it is necessary to purchase The drugs are not the same, but in the prior art, the above situation is not considered, so the fast and accurate prediction of various drugs is not used

[0014] 4. In the existing technology, although there are also methods of using big data to predict the technical application of purchasing medicines, on the one hand, it does not consider possible special circumstances, such as sudden diseases, based on abnormal diseases. If the data here is in When using the neural network to predict drug purchases, it is easy to lead to inaccurate drug predictions, resulting in lack of drugs or surplus waste

[0015] 5. In the prior art, the allocation of less used medicines is not well resolved, which easily leads to the lack of medicines in hospitals, and is not easy to find, but is easy to waste. There is no effective solution in the prior art

But so far, there is no effective way to solve the above technical problems in the prior art

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