Artificial Intelligence for Better Clinical Trial Prediction
From Life Science
The clinical trial prediction (CTP) engine empowers your decision-making: Predict the probability of clinical trial success, Evaluate-investment-decisions, Track clinical trial KPIs and identify critical path. Leverage AI to predict clinical trial outcomes: There are a lot of statistics about drug development – and all of them show that the process is inefficient and expensive: Pharma companies spend more than USD 1 Billion on developing a new drug, Drug candidates pass the clinical stage with a success rate as low as 10-15%, Of all US clinical studies, 86% fail to meet the recruitment targets on time, Dropout rates of clinical trials commonly range between 15-40%, Of failed trials, 57% show limited efficacy; poor statistical endpoints or underpowered samples.
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However, clinical trial failures can be modeled by applying Artificial Intelligence techniques on real-world, outside-trial, and biomedical data sets. Innoplexus’ Clinical Trial Prediction engine leverages advanced deep learning techniques trained on publicly available trial data as well as on real-world events that are continuously crawled, aggregated, and analyzed by our proprietary technology.
The CTP Engine Leverages Advanced Ai and Analytics Technology
Advanced deep learning techniques trained on publicly available trial data and real-world events that are continuously crawled, aggregated, and analyzed by Innoplexus’ proprietary technology.
A neural network trained on various drug compound characteristics, clinical trial features, and sponsor track records, provides insights for optimizing study designs.
Fully automated, continuous, and real time analysis enables Innoplexus to calculate predictions by accounting for new information which might impact a trial endpoint.
Leverage CTP Engine to Identify Healthcare Stock Rises
Innoplexus’ CTP model forecasts the outcome of clinical studies and may serve as an early indicator for future stock movements.
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