An end to end system for Researchers to search, collect, report and develop criteria for Disease specific evidence and provide direct to Patient remote clinical trial
By Wessam Sonbol On Jan 11, 2017
Natural language processing is a form of artificial intelligence, allowing computers to derive meaning from human input based on machine learning and implementing specific rules. NLP comes in different forms:
NLP as a technology, has been around since the 1950s and over the years, people have expanded its use
Using NLP in research can be a HUGE time saver. We live in the world of data, where every 20 minute a new publication is posted and every year over 40 new clinical trials are registered. The amount of data that we have access to today is much larger than we can imagine, making it more difficult for us to make a better informed decision
NLP can help us sift through the mountains of data that exist across publications, clinical trials and other digital means. For the purpose of this blog, we used our NLP solution "Perta" to demonstrate.
Lets say a user is searching for information about "Heart Attack", how do you ensure that other terms such as "Myocardial Infarction" are also captured in the search? Most likely the user will need to make different searches to make sure not data is left behind. Also, how can one ensure no duplicate data are in the output across the various searches? It's hard and requires a lot of manual intervention.
The example below shows the utilization of Natural Language Processing against millions of publications and 100's of thousands of clinical trials. The example below is using Delve Health's PERTA solution, structuring the user's question and quickly identifying what Perta searched for.
Looking at the search above, we only entered the condition "Heart Attack", the intervention "Surgery" and looking for "Stenosis".
The system translated the search using our NLP algorithms, providing information on "Myocardial Infarction", "Pahtologic Constriction", "Procedure" and other fields to make sure that the user did not miss relevant details.
Perta searches saves hours and days worth of work. Imagine if you had to conduct such searches manually. How many hours would this have taken? Here is what one user told us.
What I usually do in a day and half, I was able to do in 4 hours.
The example above used Natural Language Processing algorithms, translating what users are looking for to a more robust search, ensuring pertinent information are found quickly and easily.
Another use of NLP is parsing of data and naming recognition. The example below shows how we can search for one person across millions of publications and over 250,000 clinical trials, ensuring that the system can translate the name into several variations.
Check out our case study here
NLP has proven to accelerate the decision making process and help save time, but it is not magic. A lot of algorithms need to be defined, developed, tested and executed.
The use of NLP will continue to grow and help clinicians identify better treatment options faster, symptoms, diagnoses, and adverse events to support safety surveillance.
Our aim is to accelerate drug development and save more lives.