When a pharmaceutical company develops a drug, it needs to pass through several phases of 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 Boehringer Ingelheim, I developed and trained a deep learning tool 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 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 I will be glad to discuss.
