The significance of data in Medicine

Every healing system in general, works towards the goal of relief and cure from diseases. Prevention and management of diseases through coordinated methods aims to restore health by working on the underlying causes of the disease being treated.However, for sustained improvement in the approach, collaboration with data analysis paints a rosy picture. The potential of data mining and analysis in identifying inherent patterns from past data and thereby improving the existing line of treatment is noteworthy.

Data Analysis

At Oxyent, we've a system of data analysis solutions that provides invaluable insights which are significant with respect to both business as well as scientific goals. From the data, it is possible to identify the best practices or trends that could potentially improve consulting outcomes. The insights and observations taken out could go a long way in improving the effectiveness of doctors and thereby indirectly leading to patient repeatability and ultimately better revenue generation.


The biggest challenge lies in data understanding. Data understanding is achieved through exploratory analysis and experiments to identify key patterns which could turn out as productive. The analysis begins with identifying the key modules in data to focus, and then analysing the distribution of data from the perspective of each of these key modules. To communicate the outcome of analysis clearly and effectively, different data visualizations of data like bubble charts, alluvial charts and perceptual charts.The data exploration is carried out using R and it works directly on the data through integration with mysql.

The data is then preprocessed using R to remove any inconsistencies which may exist. The data understanding phase provides an opportunity to identify key features in the data on which the focus should be directed to address the problem at hand.The preliminary insights and observations extracted helps in better understanding of data as well.

This lays down the basis for building rule sets and powerful recommender systems relevant for the treatment methods. This could be crucial in assisting the practitioner to reconfirm and adopt the suitable line of treatment. The recommender system is modelled based on similarity analysis of the predominant features in the data and calculating the relevance and diversity for the same. Following which, prominent algorithms ranging from simple Association mining to powerful Collaborative filtering are adopted to build the system.