Investigating Deep-Learning NLP for Automating the Extraction of
  Oncology Efficacy Endpoints from Scientific Literature
Aline Gendrin-Brokmann Eden Harrison Julianne Noveras Leonidas Souliotis Harris Vince Ines Smit Francisco Costa David Milward Sashka Dimitrievska Paul Metcalfe Emilie Louvet
Benchmarking drug efficacy is a critical step in clinical trial design and
planning. The challenge is that much of the data on efficacy endpoints is
stored in scientific papers in free text form, so extraction of such data is
currently a largely manual task. Our objective is to automate this task as much
as possible. In this study we have developed and optimised a framework to
extract efficacy endpoints from text in scientific papers, using a machine
learning approach. Our machine learning model predicts 25 classes associated
with efficacy endpoints and leads to high F1 scores (harmonic mean of precision
and recall) of 96.4% on the test set, and 93.9% and 93.7% on two case studies.
These methods were evaluated against - and showed strong agreement with -
subject matter experts and show significant promise in the future of automating
the extraction of clinical endpoints from free text. Clinical information
extraction from text data is currently a laborious manual task which scales
poorly and is prone to human error. Demonstrating the ability to extract
efficacy endpoints automatically shows great promise for accelerating clinical
trial design moving forwards.