Sr Advisor to the CEO at Avenga
As companies grasp unstructured data’s value and AI-based solutions to monetize it, the natural language processing market, as a subfield of AI, continues to grow rapidly. With a promising $43 billion by 2025, the technology is worth attention and investment. How can businesses leverage NLP? What are the main areas of natural language processing applications? Having first-hand experience in utilizing NLP for the healthcare field, Avenga can share its insight on the topic.
The amount and availability of unstructured data are growing exponentially, revealing its value in processing, analyzing and potential for decision-making among businesses. NLP is a perfect tool to approach the volumes of precious data stored in tweets, blogs, images, videos and social media profiles. So, basically, any business that can see value in data analysis – from a short text to multiple documents that must be summarized – will find NLP useful.
Advanced systems often include both NLP and machine learning algorithms, which increase the number of tasks these AI systems can fulfill. In this case, they unpuzzle human language by tagging it, analyzing it, performing specific actions based on the results, etc. Think of Siri or Alexa, for example. They are AI-based assistants who interpret human speech with NLP algorithms and voice recognition, then react based on the previous experience they received via ML algorithms.
To dive a bit deeper, the role of machine learning for natural language processing and text analytics lies in improving NLP features and turning unstructured text into valuable insights. So, a common approach looks like this: you train a model to perform a task, then verify the model is correct and apply it to the problem. Here are the main tasks fulfilled with the help of NLP.
→ Read how NLP social graph technique helps to assess patient databases can help clinical research organizations succeed with clinical trial analysis.
The value of using NLP techniques is apparent, and the application areas for natural language processing are numerous. But so are the challenges data scientists, ML experts and researchers are facing to make NLP results resemble human output.
→ Read our article on Pharma Manufacturing – Improving the risk-reward calculus for clinical trials: How natural language processing and machine learning can boost success in drug development by Michael DePalma and Igor Kryglyak
Despite years of research and more advanced AI, natural language processing is still not easy. Hundreds of languages with their own syntax rules are just the tip of the iceberg. Every application area has issues that make NLP models imperfect and needing improvement. In addition to the challenges we mentioned earlier, here are some of the most significant reasons NLP is not yet mainstream:
Despite these difficulties, NLP is able to perform tasks reasonably well in most situations and provide added value to many problem domains. While it is not independent enough to provide a human-like experience, it can significantly improve certain tasks’ performance when cooperating with humans. Avenga’s experience proves this statement.
→ Discover the sentiment analysis algorithm built from the ground up by our data science team.
Clinical research organizations can benefit greatly from deploying AI-powered systems for clinical trials. They help overcome medical research stagnation by enrolling a sufficient number of relevant patients for credible trial results, which is a huge advantage these days. Natural language processing helps Avenga’s clients – healthcare providers, medical research institutions and CROs – gain insight while uncovering potential value in their data stores. By applying NLP features, they simplify their process of finding the influencers needed for research — doctors who can source large numbers of eligible patients and persuade them to partake in trials.
NLP and ML help optimize and simplify daily operations, provide more value to patients and enable efficient and rewarding work for personnel.
Avenga’s NLP services include:
Explore how technology can equip and complement biotech and pharma companies seeking facilities to run their clinical trials with the utmost efficiency. If you decide to develop a solution that uses NLP in healthcare, we will be here to help you.
Natural language processing can bring value to any business wanting to leverage unstructured data. The applications triggered by NLP models include sentiment analysis, summarization, machine translation, query answering and many more. While NLP is not yet independent enough to provide human-like experiences, the solutions that use NLP and ML techniques applied by humans significantly improve business processes and decision-making. To find out how specific industries leverage NLP with the help of a reliable tech vendor, download Avenga’s whitepaper on the use of NLP for clinical trials.
We created for you a 25-pager whitepaper discussing the most important new technologies that are transforming the clinical trial processes. Bring treatments to patients faster and at a lower cost.