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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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MEDICINE INFORMATION HUB

Authors: 1, Mrs M.Praveena(M.E).; 2, Mr.G.Shiva kumar M.E(ph.D); 3, M.sasi kumar; 4, R.U.varun; 5, T surya;

MEDICINE INFORMATION HUB

Abstract

The Medicine Information Hub using Artificial Intelligence and Machine Learning is a digital healthcare information system designed to provide accurate and reliable details about medicines. The main objective of this project is to create an intelligent platform that helps users easily access information related to medicines such as usage, dosage, side effects, precautions, and alternative drugs. In today's healthcare environment, patients often search for medicine information on the internet, where inaccurate or incomplete data can lead to misunderstandings and potential health risks. To address this issue, this project proposes an AI-based system that collects, processes, and organizes medicine-related information into a centralized platform called the Medicine Information Hub. The system utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze medicine data and provide useful insights for users. Machine learning algorithms help classify and retrieve relevant information efficiently based on user queries. Natural Language Processing (NLP) techniques can also be applied to interpret user inputs and deliver accurate responses regarding medicines. The platform stores medicine data in a structured database that includes details such as the drug name, composition, therapeutic uses, recommended dosage, possible side effects, contraindications, and precautions. When a user searches for a medicine, the system processes the query and retrieves the relevant information from the database quickly and efficiently. One of the key features of the Medicine Information Hub is its ability to improve information retrieval using machine learning models. These models help organize medicine data, detect similarities between drugs, and recommend alternative medicines when required. This feature can be particularly useful for patients, pharmacists, and healthcare professionals who need quick access to medicine details. The system is designed to be user-friendly and accessible through a web-based interface. Users can search for medicines using simple keywords, and the platform displays detailed information about the selected drug. This reduces the dependency on multiple sources and ensures that users receive reliable medical information in one place. Additionally, the system can assist in improving public awareness about medicines by providing clear explanations of drug usage and safety precautions. This can help patients better understand their prescriptions and avoid misuse of medications. The project is developed using modern programming and data science tools such as Python, machine learning libraries, and database management systems. These technologies enable efficient data processing, intelligent information retrieval, and scalable system performance. Overall, the Medicine Information Hub using AI and ML demonstrates how artificial intelligence can be applied in the healthcare domain to simplify access to medical knowledge. The system aims to enhance healthcare awareness, support informed decision-making, and provide a reliable digital platform for medicine information.

Keywords

Artificial Intelligence, Machine Learning, Healthcare Information System, Medicine Information Hub, Drug Information System, Natural Language Processing (NLP), Medical Data Analysis, Intelligent Healthcare System, Medicine Recommendation System, Drug Safety Information, Healthcare Technology, Medical Knowledge Retrieval, AI in Healthcare, Smart Medical Information System, Digital Healthcare Platform.

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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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