Abstract
HealthDesk is a mobile solution for all health-related queries. The application is for all the users of the healthcare system. It covers all the major domains in the hierarchy. People have now started digitizing in this domain too. People have shown great trust in this. This helps us to provide the best. Despite this, individuals often face problems ordering their medicines online. There are high chances of people taking medications without a prescription. So, this application recommends top medicines that are similar to the one being ordered to avoid drug-drug interactions. In the scenario of patient emergency many applications for patient health monitoring and appointment scheduling have been developed. However, in the wake of an emergency, people tend to blank out or are unaware of nearby emergency services. So, the application has a feature that enables users to search the most nearby doctor and provide the doctor with the user's current location. The user can be provided with first aid immediately so that he doesn't succumb to death. The healthcare system has frequent updates. The doctors must remain at par with the updates. However, doctors find it strenuous to sit by and search. The app provides them with relevant news according to their preferences. Concluding, this app covers the most important stakeholders of the healthcare system.
Highlights
HealthDesk is a mobile solution for all health-related queries
This paper presents the risks about self-medication and proposes a machine learning approach to reduce the risks associated
This paper provides the implementation of an android application in order to provide a solution to the concerned issue
Summary
Changing governmental regulations and models of care. Lifelong learning is not an option in healthcare, it’s required by. Drugs, being a basic entity for medical evaluations become an important target here to be extracted effectively This Paper mentions an approach termed Drug Name Recognition (DNR), immediately search for a doctor in your shortest distance and provide the doctor the user’s current location. BIO: PXRligands\O include\O a\O wide\O variety\O of\O pharmaceutical\O agentst\O .\such\O as \O antiepileptic \Bgroup drugs \ I-group ,\O taxol\B-brand \O rifampicin\Bdrug\O and\O human\ B-group immunodeficiency\l-group virus \lgroup protease\l-group inhibitors\I-group such\O ritonavir\B-drug and \O saquinavir \B-drug .\O This tagging shows us to extract the drug names and so would use it for further implementation to achieve the objective.[1]. This paper has the main motive to present a tool that helps in better decision making as to which counter medications to buy This is done by classifying drugs into different classes/clusters and providing relevant info like class, description, use of the drug. The User credentials, doctor credentials, which doctor is currently assigned to which patient and all such information is needed to be effectively stored.[3]
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