The combined value of NLP and Machine Learning – a concrete example
With the rising costs of de novo drug discovery, and increasing focus on rare diseases, there is continuous innovation for methods and solutions to find new uses for existing drugs. I was interested to hear of a novel approach for this, published recently by Eric Su and Todd Sanger at Eli Lilly. In this paper, “Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov”, the authors describe the combined use of Natural Language Processing (NLP) and Machine Learning (ML), to extract potential new uses of existing drugs.
It’s quite astonishing how often in the last weeks and months I’ve been asked about the interplay between NLP, Artificial Intelligence (AI), and ML. It seems that everyone wants to understand more about the real potential (rather than the hype that is being shouted from the rooftops) that these tools will provide to impact healthcare, research, and many other areas of our lives, in the next decade.
So, let’s delve further into this concrete example of the combined value of NLP and ML. The innovative step here was to exclude trials for a specific indication, such as cancer, and then find trials with Serious Adverse Events (SAEs) classified as cancerous. The researchers then looked to see if the placebo arm had more cancerous SAEs. If the placebo arm had more cancer-related SAEs than the treatment arm, they hypothesized that the treatment has a positive anti-cancer effect.