Prepare Effective Clinical Trials With Machine Learning

Run Custom Queries Through a Powerful API

Query the clinicaltrials.gov database for historical and active clinical trials. Build streamlined data flows through REST API and initiate data ETL to build site intelligence through an integrated Dataiku Application.

Build Aggregate and Individual Study Data

Address inherent quality issues by harmonizing data such as clinical site IDs MeSH terms. Get deeper clinical site analytics with feature engineering and ML for enrollment rate predictions and study similarity indexing.

Get Deep
Sponsor Insights

Get comprehensive sponsor insights on studies and associated clinical sites and create a dashboard that combines competitive intelligence of site utilization by competing sponsors. Make even deeper connections with Social Determinants of Health (SDoH) by including US Census and CDC data to inform sites that support diverse enrollment.

Explore Studies in Depth

Use a dynamic web application to enter a novel study synopsis from your clinical planning or feasibility team to clinicaltrials.gov, or choose similar studies and their sites, then review comprehensive analytics dashboards.

Study Similar Analyses

Use ML models for study enrollment rate prediction and find similarities based on existing protocols or novel study synopses. Find sites with similar conditions or participation criteria, and filter for further analysis.

Get Detailed Community & Historical Site Analysis

Study comprehensive analytics, community social measures (US only), and historical activity for individual sites leveraged in relevant selected similar studies.

Answer Key Clinical Trial Questions

With the Dataiku Solution for Clinical Site Intelligence, answer a broad range of questions like:

• What are the potential sites I can use for my clinical study protocol?
• How have sites conducting similar trials performed historically?
• How are the sites I’m using in my clinical trials used by competing studies/sponsors?

Improve Your Clinical Operations, and More

Adopt a data driven approach to Clinical Sites Selection and revisit your trials practices. Enrich with more data sources, easily develop new ML-enriched analytics, and embark on a full journey to enhancing your key processes with AI.