Predicting clinical trial complexity

Predicting clinical trial complexity

Vertical

Healthcare

Industry

Pharmaceutical

Client

Boehringer Ingelheim

Predictive models use many techniques ranging from Data Mining to Machine Learning (ML) and Artificial Intelligence (AI).

These models find patterns in historical clinical trials data and the latest advancements in drug design to find an eligible patient for a trial. Predictive models primarily capture relationships among many factors to assess the risks. It makes this branch of data analytics well-suited to address the most profound challenges that researchers face in clinical trials.

We developed NLP (natural language processing) models to predict 75 complexity metrics from a clinical trial protocol, which is a 200-page PDF in technical English. This drastically increased the speed of research design.

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