Recommending best course of treatment based on similarities of prognostic markers

TL;DR

This paper introduces a collaborative filtering recommender system for healthcare that suggests treatments based on patient prognostic markers, utilizing a newly developed dataset and demonstrating promising experimental results.

Contribution

It presents a novel healthcare recommender system using collaborative filtering and introduces a new dataset for remedy prediction based on prognostic markers.

Findings

Achieved promising accuracy in remedy recommendations

Developed a new dataset of remedies and diseases

Demonstrated effectiveness through experimental trials

Abstract

With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to propose efficient solutions for data retrieval. This means that from an enormous pile of data, the retrieval methods should allow the users to fetch the relevant and recent data over time. In the field of entertainment and e-commerce, recommender systems have been functioning to provide the aforementioned. Employing the same systems in the medical domain could definitely prove to be useful in variety of ways. Following this context, the goal of this paper is to propose collaborative filtering based recommender system in the healthcare sector to recommend remedies based on the symptoms experienced by the patients. Furthermore, a new dataset is developed consisting of remedies concerning various diseases to address the limited availability of the data. The proposed recommender system accepts the prognostic markers of a patient as the input and generates the best remedy course. With several experimental trials, the proposed model achieved promising results in recommending the possible remedy for given prognostic markers.