Cosine Similarity Based Word2Vec Model for Biomedical Data Analysis

Abstract

The conversion process of words to vectors involves mapping each word or term in the biomedical data to a unique numerical vector, called a word embedding. One of the key advantages of using word embeddings is that they capture the meaning and context of words in a numerical format, which can be used as input for various machine learning and data analysis tasks. The Milvus vector engine is an open source software that allows for the efficient and accurate representation of large-scale data in vector space. It is particularly useful for high-dimensional data such as biomedical data that contains a large number of unique words and terms. The use of the Milvus vector engine is to convert biomedical data into numerical vectors for machine learning and data analysis tasks. In the context of biomedical data, the use of word embeddings can enable new discoveries and insights by allowing for more accurate and efficient analysis of large datasets. This word embeddings can be used to identify patterns and relationships in biomedical literature, or to classify medical articles based on their content.