Design and Optimization of Clinical Trials

Drug clinical research stage includes trial scheme design, trial process management, statistical analysis of trial data management, etc. if only relying on manpower, it is not only heavy workload, but also easy to make mistakes. Artificial intelligence has the ability of machine learning and cognitive computing, which can be widely used in various links such as experimental research design, experimental process management, and experimental data statistical analysis. CD ComputaBio can help our clients to improve the efficiency of clinical trial in the whole process, so as to accelerate the process of drug research and development.

Highlights

Eliminate the barriers of isolating clinical medical data in various medical institutions.
Make patients more easily participate in clinical trials and eliminate unnecessary burden of clinical operation.
Enable researchers to reduce the rate of clinical trial lost visits through personalized communication.

Key Capabilities

CD ComputaBio uses complex data analysis methods and machine learning technology to analyze these data comprehensively, and to maximize their potential to improve the possibility of clinical trial success and reduce the cost of drug research and development process.

Data Collection

Big data collection, guide clinical trials intelligently and put forward new hypothesis that can be verified.

Deep Learning

AI can extract knowledge that can promote drug research and development from a large amount of information of papers and clinical trial results.

Optimized Algorithm

Structured and unstructured biomedical and healthcare data streams of clinical trial participants can be transformed into computer models that represent individual patients.

Artificial Intelligence in Design and Optimization of Clinical Trials

Use AI technology to analyze a large number of clinical data, and find important information to help clinical trial design and drug development.
Collect and analyze the data isolated in pharmaceutical companies, hospitals, universities, genomics companies and other biomedical institutions, so as to obtain new knowledge that could not be discovered before.