Identification of chemical entities from prescribed drugs for ovarian cancer by text mining of medical records
Sonali Kothari, Vijayshri Nitin Khedkar, Anvita Gupta, Adhiraj Dev Goswami, Muskaan Agrawal, Kajal Jaggi · 2022 International Conference on Decision Aid Sciences and Applications (DASA) · 2022
With the emerging technologies and latest trends in the information technology sector, machine learning can be said as one of the most powerful means in developing various software which are helping the business as well as the industries, the government, and private organizations, which shows the change in the emergence of this technology in the healthcare sector. Healthcare can be said to be the most important aspect of the 21st century. With the spread of various diseases and also the pandemic which had hit the world recently the healthcare system must come up with efficient drugs which are liable and are for a sure cure to the disease. Proposed research involves use of various machine learning tools and deep learning techniques. The algorithm uses text mining and NLP for extraction of chemical data from the related research papers and a drug dictionary is created. This dictionary contains specific targeted chemical data related to drugs used for treating ovarian cancer and will help in suggesting personalized drugs to patients. Our primary focus is ovarian cancer. Ovarian cancer is not only deadly to women but also very tough to get the correct diagnosis. It is considered the third most crucial cancer within Indian women.