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Drug effectiveness prediction software

Prediction software that played a key role in achieving a 46% decrease in the likelihood of treatment-related side effects, enhancing overall patient safety and reliability.

Client

Pharmaceutical company specializing in oncology, gastroenterology, and neuroscience drugs development.

Objective

Determine the probability of negative response to components of several drugs developed to treat lung cancer, epilepsy and measles.

Solution

Our team collected data samples from multiple sources including company R&D department, observational studies, EHRs, hospitals and public databases. We then cleaned and labeled the data for effective analysis. Using various Machine Learning algorithms, techniques and frameworks, our data scientists built a model that was able to predict drug effectiveness across a range of parameters. On top of that, the solution can suggest alternative clinical trial approaches in case of poor treatment outcomes.

Result

Our AI-powered drug effectiveness prediction software has proven instrumental in improving treatment outcomes. By leveraging advanced Machine Learning techniques and data-driven insights, we not only increased treatment efficiency but also reduced the risk of side effects for patients undergoing drug therapies.

  • Increased treatment efficiency: the implementation of the AI-powered software led to a 28% improvement in treatment efficiency for widely spread diseases, significantly enhancing the overall effectiveness of medical interventions.

  • Side effects risk reduction: by accurately predicting drug effectiveness, the software contributed to a 46% reduction in the risk of side effects associated with the treatment, ensuring a safer and more reliable patient experience.

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