I2E NLP in action - Extracting Laboratory Eligibility Criteria Data Elements from Internal Review Board ProtocolsLinguamatics
“It is easy to lie with statistics. It is hard to tell the truth without it.” -Andrejs Dunkels
This is a quote I first heard long ago, but was recently re-introduced to by a beloved colleague of mine. Anyone with a background in research can attest to just how true this quote is. Without good statistical power, life-saving pharmaceuticals never make it to the market. Undoubtedly, the ones that do, do so at a hefty cost. In 2012, Forbes.com published an article reporting that the average cost to develop a new pharmaceutical was $4 Billion, and could reach upwards to $11 Billion, staggering numbers, and that was 4 years ago. Without any hesitation, I can confidently say, “those numbers aren’t going down.”
But WHY do pharmaceuticals cost so much?
There are genuine factors that contribute to these huge costs, and one of the most expensive phases of drug development are clinical trials. Those of us that have worked in research know that clinical trial recruitment is a huge factor that takes an exorbitant amount of time and money. If you don’t get enough eligible people successfully recruited, and finished in the study, the study won’t have the all-powerful “n”, the number of people that statistically is needed to prove that the study drug was safe and effective (or not).
How can Natural Language Processing (NLP) help in recruitment?