How Can NLP aid in Clinical Trials?

With 2020 being one of those years that has resulted in giving importance to even the minutest of things, the healthcare sector has witnessed pressure like never before. The pandemic is all the more a strong reason as to why clinical trials have gained popularity and importance with the course of time. However, this isn't a cakewalk. So far, the records say that most clinical trials fail because they either don't demonstrate the efficacy or the safety of an intervention. Some other reasons that lead to the trials failing could be shortage of money, a flawed study design, participant drop-outs to name a few. Some cases also see the failure to recruit enough volunteers in the first place as the reason why clinical trials end up unsuccessful.

Consider the case of the 2020 pandemic. The extent to which this has hit the entire world needs no special mention. Since, it is the entire world to be taken into account, the first challenge faced would be in the case of entering and transferring data. Next up is ensuring that everyone takes the correct dosage. No wonder, delays, inaccuracies and inefficiencies are bound to occur.

To improve the clinical trials, researchers are now turning towards Artificial Intelligence. Natural Language Processing (NLP) is that branch under AI that is known to achieve targets and objectives like never before. NLP enables computers to analyse written or spoken human language to further extract a meaning out of it. All this paving the way for obtaining useful insights from the data collected.

How can NLP aid in clinical trials?

NLP when applied to the field of medicine has the potential to allow algorithms to be able to search doctors' notes and pathology reports for people who would be eligible to participate in a given clinical trial.

Another point worth noting is that most of the medical data obtained is unstructured and cannot be used directly to draw meaningful insights. This is exactly where modern NLP techniques come to the rescue. With these techniques, it is possible to process and analyse clinical documentation followed by extracting the required information. Also, most of these techniques promote automation, thus eliminating the time researchers have to spend to get the work done.

Some of these modern NLP techniques that can be employed are –

• Keyword extraction: As the medical data obtained isn't structured and is lengthy enough to be able to go through every single thing in particular, keyword extraction serves to be the saviour. With this, it is extremely easy to extract the required information from unstructured data. Undoubtedly, it saves a lot of time for the professionals who are conducting the trials.
• Named entity recognition: This techniques allows for the identification of parameters like the name of the doctors, patterns, locations, drug components and other objects that may be of interest.
• Semantic parsing: Unstructured clinical data is not the easiest to deal with. Thus, techniques that make the best possible use of this data are critical. With semantic parsing, it is possible toproduce precise meaning representations from unstructured clinical trial data. In other words, converting natural language utterances into logical forms is the key objective.
• Topic modelling: This is yet another useful technique which makes the entire clinical trial smooth. Topic modelling allows the researchers to conduct topic segmentation and recognition thereby making it extremely convenient to automatically define what topics were used.

Needless to say, clinical trials are way more important than one thinks they actually are. But, the question that still remains is, how to best deploy techniques to be able to achieve smooth clinical trials. NLP has surely carved a niche for itself in the healthcare domain. Proper use of the same will help achieve results that will change life for better!