Context Based Article Search Engine Using Word2Vec Models

The efficient search and retrieval of relevant medical
literature is crucial in the healthcare industry, where
access to accurate and up-to-date information can
significantly impact patient outcomes. However, the
complexity and volume of medical literature make it
challenging for healthcare professionals to quickly
find relevant information. By developing a search
engine powered by domain-specific medical word
embeddings, we aim to bridge this gap and provide a
tool that enables fast and accurate searching of
medical literature. This project is motivated by the
need to improve the efficiency and effectiveness of
medical research and clinical decision-making,
ultimately leading to better healthcare outcomes.
We are using word2vec models to find semantic
similarity based on the search query.
This application is a demo of the possibilities of researching articles in digital domain. We believe that a subject matter expert would have added a lot more in the developement of the application. The use of this application would be a great fit for university or public libraries or any scope for searching context based documentation.