Machine learning seamlessly is integrating with other industries right before our eyes. Like with so many so many other industries, more data means greater effectiveness in the pharma industry. A McKinsey report estimates that machine learning and big data could generate a combined business value of $100B annually. The value optimization involves optimal innovation, better decision making, greater efficiency for clinical/research trials, and additional tool creation for medical professionals.
What is the source of so much data? Regular streams like research and development, clinics, physicians, patients, and caregivers do their bit. The disparate origin points of data sets form a large part of the problem when we talk of synchronizing all the data and improving the healthcare industry as a whole. The core of the problem is to find ways to effectively collect different types of data sets for better treatment, analysis, and ultimately treatment.
Today, the applications of Machine Learning are sprouting in manifold ways. All these applications give us a glimpse of a future where the analysis and synchronization of data is already a reality. Here’s a collection of some of the most important applications of machine learning in the pharma industry.
1. Behavioral Modification
With machine learning, personalized medication will soon be a reality. Such treatment is based on individual health data along with a decent dose of predictive analysis. In fact, this is one of the most hotly worked on topics on machine learning and behavioral modification.
At the moment, this area is dominated largely by a combination of genetic information and supervised learning. This basically allows physicians to choose from a set of diagnoses. The next decade is crucial for health optimization with the help of machine learning. Micr biosensors will see a further rise in the application and there will be a similar rise in use remote monitoring capabilities.
A gamut of startups is emerging in fields like cancer identification and treatment. While the success of these innovations is still far from desirable, there is a good chance of a significant breakthrough in the coming decade.
2. Research and Clinical Trial
Machine learning has already tasted decent success in shaping direct research and clinical trial. The application of predictive analytics in identifying candidates for clinical trials could see a lot of additional data pouring in compared to the volume of data we see today. Clubbed with genetic information, this will help in quicker and more cost-effective trials in the coming days.
The application of Machine Learning also spans over access to real-time data for heightened safety. One critical area is the monitoring of biological signals for any visible sign of harm or fatality. McKinsey suggests that there is a whole lot of other applications that help in augmenting the efficiency of a clinical trial. This includes the discovery of the best sample sizes for greater efficiency.
3. Drug Manufacturing
ML has tremendous scope in the early stages of drug discovery. The application starts at the initial screening of compounds and moves over to the predicted success rate based on a number of biological factors. We are also looking at discovery technologies in R&D like next-generation sequencing.
Precision medicine, a genre that involves mechanism identification for multifactorial diseases, is the frontrunner in this race. Since a lot of this research is based on unsupervised learning, the game revolves around identifying data patterns without the use of prediction of any kind.
4. Identification and Diagnosis of Disease
Most of the present efforts in Machine Learning research for the pharma industry is geared toward disease identification. In a 2015 report released by the PRMA, the number of cancer medicine and vaccines in trial went over 800. The larger challenge, however, is to make justified use of all the data that comes out as a result of these studies.
It is here that the need for biologists working with information scientists and machine learning experts will become extremely vital. It doesn’t come as a surprise that the bigger players were the first to jump on the bandwagon. IBM Watson Genomics came into existence in 2016. It partnered with Quest Diagnostics to take rapid strides in precision medicine.
5. Epidemic Control
At present, AI and ML technologies are also being used to monitor and foretell epidemics around the world. The predictions are based on satellite data, real-time updates on the social media as well as other sources. Scientists have already made use of artificial neural networks and support vector machines to predict outbreaks of malaria. The analysis took into account factors like average monthly rainfall, temperature, data points, and the number of positive cases.
The prediction of outbreaks (and their severity) becomes even more important in third-world countries where epidemics claim lives in the hundreds. This also alerts governments to implement prevention protocols and if needed quick treatment measures, too.
Futran Solutions works with a talented pool of resources that are ML experts in the pharma industry. Call us today to know how we can help you with project implementation and digital consulting in machine learning.
Jyoti Vazirani is the co-founder of Futran Solutions. She is a certified SAFe Agile coach and an out and out machine learning enthusiast.
