Tags:COVID-19, Decision Tree, Drug efficacy, Exploratory Data Analysis, HCQ, Machine Learning and Remdesivir
Abstract:
In this paper, symptoms and medical treatment data of 130 COVID-19 patients have been collected from a leading Hospital in Kolkata. After necessary de-identification and data wrangling, a thorough exploratory data analysis has been performed. Further, it has been investigated if the drug Remdesivir affects early discharge. A decision tree-based model was subsequently built to predict the length of stay of a patient, based on demographics and health parameters. It is observed that Remdesivir cannot be concluded to be more effective than alternative treatments. It is observed that diabetes significantly increases the length of stay of a patient. It may be noted that such a study has not been conducted earlier for COVID-19 patients in India. This study will be beneficial for the healthcare community & pharmaceutical companies as there is a lot of conflicting views and an acute dearth of information about the disease and its treatment.
A Data Science Approach to Evaluate Drug Effectiveness: Case Study of Remdesivir for Covid-19 Patients in India