Abstract
From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.
Highlights
Counterfeit drugs are a very big challenge for the pharmaceutical industry worldwide.One question arises here: what does Counterfeit drug mean? According to World Health Organization (WHO), “The drug which is manufactured fraudulently, mislabeled, with low quality, hiding the source detail or identity and does not follow the defined standard is considered to be fake or Counterfeits” [1]
We introduced a machine learning-based recommendation system that is trained using previously collected drug user reviews data-set: the data-set was collected through web crawling and contains the comments and ratings based on the disease condition of the consumers
We summarize the blockchain applications which are currently used in the healthcare field, to give a glimpse into the mist-filled blockchain technology application field, which belongs to the future development direction of the healthcare industry [17,20]
Summary
Counterfeit drugs are a very big challenge for the pharmaceutical industry worldwide.One question arises here: what does Counterfeit drug mean? According to WHO, “The drug which is manufactured fraudulently, mislabeled, with low quality, hiding the source detail or identity and does not follow the defined standard is considered to be fake or Counterfeits” [1]. The survey provided from the WHO stated that in underdeveloped countries every 10th drug use by the consumers is counterfeit and has low-quality [2] This WHO claim may not be perfect or have more than this because there is no accurate estimation or statistics of counterfeits drugs. Sometimes the manufacturer of these fake medicines used the logo of some reputed and popular pharma company to enter their drugs in the market without facing hurdles. These drugs affect the sales of popular and expansive medicines like antibiotics, cancer, painkillers, many other cardiac medicines, etc., and have a lot of side effects and cause more serious health problems. Almost 10–15% of the drugs are fake worldwide but in the case of developing countries, the ratio of these drugs are more around 30%
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