A Machine Learning Framework to Predict Adverse Drug Reactions from Electronic Health Records

Abstract

The advent of medical industry is immense in the recent years, with increasing epidemics, pandemics and endemics, thereby ensuring proper treatment to the patients with affordable medications. Right from the chemical compositions, the manufacturing involves a huge procedural overhead along with the clinical trials and pre-marketing studies. Such medications have to clear stringent norms and policies in order to reach the market, during which, the medication should exhibit compliance to lesser side effects, and increased control over the diseases. Adverse Drug Reactions (ADR) are typically reactions which are unintended and may result in harmful effects over the patients. Multiple models understand the data collected from various sources to monitor the effects of medications in regular intervals, in order to prepare for counter actions. The risk of side effects and adverse drug reactions can be reduced with timely detection of drug to protein interaction and drug to drug interactions. It is a practice of co-prescriptions for addressing multiple medical conditions in elderly people and patients with multiple ailments. Compared to the previous manual techniques, prediction of adverse drug reactions was carried out using machine learning techniques lately. The proposed technique introduces a novel mechanism using regularized logistic regression technique to effectively trace the drug-to-drug interactions. The datasets are considered from openly available sources, and electronically stored information are fed into regression models for finding relevant patterns. Empirical studies applied with necessary cross validation checks and numerous failproof tests deliver promising outcomes in form of drug-ADR targeted profiles for signifying the results of the supposed study. From the investigative results, it is evident that the proposed technique ensures utmost quality and interesting insights for making appropriate biological and protein-drug based decisions.