Machine Learning (ML), Natural Language Processing (NLP), and Artificial Intelligence (AI) are related sub-fields of computer science which give the computers the ability of automatic learning of complex patterns from empirical data. Based on the learned behavior, the methods allow making intelligent decisions supporting human experts, especially in areas dealing with the substantial degree of uncertainty.
Pharmacovigilance, being a classic example of a medical domain with large dimensionality of data and variable environment, turns the decision-making processes into extremely challenging tasks.
We at RxLogix, develop and adapt ML, NLP and AI based methods for our PV applications. Our solutions do not aim to replace human experts but provide computer-aided and evidence based guidance supporting evaluations of drug related benefit-risk balance instead. Our various methodologies for trend and signal detection are used for advanced real-time visualizations, analyze clinical trials and patient records to identify follow-on indications, and discover adverse effects before products reach the market.
We at RxLogix believe that predictive analytics module will help lower attrition and produce a leaner, faster, more targeted R & D pipeline in drugs and devices. Our Software can analyze disease patterns, track disease outbreaks, transmission to improve public health surveillance, and quick response.
Faster development of more accurately targeted vaccines, for example: choosing the annual influenza strains, etc, can turn large amounts of data into actionable information. That information then can be used to identify needs, provide services, predict and prevent crises, especially for the benefit of public health and safety.
At RxLogix, we are using AI in our products which will transform the way Life sciences industry works, which is nothing short of science fiction. Our Softwares are breakthroughs in the true sense of the world, that make the higher levels of intelligence possible-but it is not magic. We at RxLogix like to think that we have solved the Achilles’ heel by getting just the right amount of huge data that is used to tackle this problem. We follow just one rule of thumb: If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI right now not in the near future.