The pharmaceutical world is not aloof from data, and as the industry grows, the amount of data available also rises.
Fremont, CA: Clinical research relies massively on empirical data to test theories and determine the effectiveness of the treatment. This opens the door for a first opportunity for pharma organizations to scale analytics adoption and inculcate more sophisticated data science techniques. Amidst the constantly evolving customer needs, medical research, and regulations, scientists and pharma professionals can undoubtedly benefit from a tool that grabs all the data and transforms it into foresight. At this juncture, predictive modeling and machine learning steps in. The incorporation of predictive analytics in the pharmaceutical industry allows getting visibility into future outcomes, forecast product demand, and monitor anomalies in real-time.
• Optimize Drug Development
Clinical research is one of the trickiest phases to optimize in a typical pharmaceutical workflow. It involves many trial and errors and has a low success rate. To enhance research operations, pharmaceutical scientists can move to machine learning algorithms for identifying potential pitfalls in the very first stages of development. By utilizing classification techniques, the organizations can get an insight into the type of drugs that can be retained, what is likely to transform the composition, as well as the chances of success in the adoption process.
• Predict Site Health for Clinical Trials
A better idea of site health can be gained via predictive modeling. By utilizing historical pharma data, machine learning algorithms can identify the factors that help to get most to the site health, and in turn, pinpoint optimal sites and determine the possibility of its clinical failure. With ML like this in place, clinical trials can happen without a glitch, the research process can be streamlined, and productivity can be enhanced.