Pharmacovigilance With Datamining And Machine Learning Algorithms

Abstract

Data mining is a relatively new field of research whose major objective is to acquire knowledge from large amounts of data. The medical and healthcare industry has generated large amount of data generated from record keeping, compliance and patient related data. In todays digital world, it is mandatory that these data should be digitalized. To improve the quality of healthcare by minimizing the costs, it is necessary that large volume of data generated should be analysed effectively to answer new challenges. On one hand, practitioners are expected to use all these data in their work, but at the same time, such data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. The increasing gap between healthcare costs and outcomes is one of the most important issues, and many efforts to fill this gap are under way in many developed countries. Very fast growth in medical data needs huge electronic medical record forms that have patient s health history. Governments take necessary steps to gather the patient s health history to carry out research and be prepared for any disease outbreaks at large to the citizens. Research has shown that the disease outbreaks are due to the lifestyle, the living conditions and the treatment undergone during the past. Medical literature states that many drugs whose complete safety profile is unknown have been approved. Some drugs have shown serious adverse events and subsequently withdrawn. There may be some drugs which still show adverse effects on the patients. Pharmacovigilance begins during clinical trials and continues after the drug is released into the market. The advent of social media offers insights into healthcare unfiltered by traditional methods of healthcare data collection. Applying natural language processing techniques to extract patient reports of adverse drug events from social media has great potential to improve clinical and scientific knowledge of Pharmacovigilance.