We used natural language processing to analyse clinical trial protocols in the pharmaceutical industry. Pharma companies write a 200-page protocol at the planning stage of a clinical trial, and our model is able to ‘read’ the document and output a number of complexity metrics.

NLP in pharma: modelling clinical trials at Boehringer Ingelheim

When a pharmaceutical company develops a drug, it needs to pass through several phases of clinical trials before it can be approved by regulators.

Before the trial is run, the drug developer writes a document called a protocol. This contains key information about how long the trial will run for, what is the risk to participants, what kind of treatment is being investigated, etc.

The problem is that each protocol is up to 200 pages long and the structure can vary.

For the German pharma company Boehringer Ingelheim, we developed and trained a deep learning tool using natural language processing (NLP) to predict more than 50 output variables from a clinical trial protocol. This allows pharma companies and regulators to analyse and quantify large numbers of clinical trial protocols, allowing more accurate cost estimation.

The technique can be extended to other industries where large unstructured or semi-structured documents are the norm.

If you have a problem of this nature please get in contact and we will be glad to discuss.

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50K employees

€15.9 billion revenue (2017)

founded in 1885

Boehringer Ingelheim – Clinical trials analysis

AI has great potential to revolutionise many aspects of the pharmaceutical industry, from pre-clinical stages such as in silico drug discovery through to clinical trials and aftermarket monitoring of key opinion leaders (KOLs). At Fast Data Science we are at the forefront of AI in pharma and have worked on projects in the pre-clinical, clinical and KOL stages of the drug development lifecycle. Read more here about how researchers are using AI in the pharmaceutical industry. We have primarily focused on NLP projects in the pharmaceutical industry but have also worked on more general data science projects such as complexity and risk estimation.

Natural Language Processing and AI in the Pharmaceutical Industry

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