Aetion has taken a significant step in the field of real-world data analysis by introducing an innovative tool that makes it easier for users to identify hidden patterns in patient populations through natural language queries. This new functionality, known as Smart Subgroups Interpreter, is based on unsupervised learning methods and generative artificial intelligence to identify groups of patients with similar characteristics from extensive data sets.
The use of real-world data is crucial for evaluating the effectiveness and safety of medical innovations, but extracting relevant information from these data can be challenging. Aetion, a leading provider of real-world evidence software, has developed its platform to turn this data into evidence that supports clinical and regulatory decisions.
With tools like Aetion Discover, researchers can conduct exploratory analyses quickly and structured. Unsupervised learning allows for the identification of smart subgroups, which are sets of patients within a larger population who share similar profiles in diagnoses, procedures, and treatments.
Recently, Aetion has enhanced its technology by integrating Amazon Bedrock and advanced language models, such as Anthropic’s Claude 3, to facilitate interaction with its systems. This enables users to ask questions in natural language about patient subgroups and receive detailed answers that stimulate the generation of new hypotheses. This approach not only improves access to data but also accelerates the research process, allowing users to produce evidence with a high degree of certainty in a matter of minutes.
Aetion’s ability to apply causal inference principles in data analysis has led the company to establish collaborations with leading biotechnology organizations, insurers, and regulatory agencies globally. Available tools, like Aetion Substantiate, allow researchers to conduct studies on the safety and efficacy of drugs and treatments in a more efficient and precise manner.
This development represents a major milestone in the use of artificial intelligence in the healthcare field, highlighting how technology can transform data analysis into valuable information, thus optimizing medical research and clinical decision-making.
Source: MiMub in Spanish
