The real-world data collected and derived from patient journeys offers a wealth of insights into patient characteristics and outcomes and the effectiveness and safety of medical innovations. Researchers ask questions about patient populations in the form of structured queries; however, without the right choice of structured query and deep familiarity with complex real-world patient datasets, many trends and patterns can remain undiscovered.
Aetion is a leading provider of decision-grade real-world evidence software to biopharma, payors, and regulatory agencies. The company provides comprehensive solutions to healthcare and life science customers to transform real-world data into real-world evidence.
The use of unsupervised learning methods on semi-structured data along with generative AI has been transformative in unlocking hidden insights. With Aetion Discover, users can conduct rapid, exploratory analyses with real-world data while experiencing a structured approach to research questions. To help accelerate data exploration and hypothesis generation, Discover uses unsupervised learning methods to uncover Smart Subgroups. These subgroups of patients within a larger population display similar characteristics or profiles across a vast range of factors, including diagnoses, procedures, and therapies.
In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence. Aetion chose to use Amazon Bedrock for working with LLMs due to its vast model selection from multiple providers, security posture, extensibility, and ease of use.
Amazon Bedrock is a fully managed service that provides access to high-performing foundation models (FMs) from leading AI startups and Amazon through a unified API. It offers a wide range of FMs, allowing you to choose the model that best suits your specific use case.
Aetion’s technology
Aetion uses the science of causal inference to generate real-world evidence on the safety, effectiveness, and value of medications and clinical interventions. Aetion has partnered with the majority of top 20 biopharma, leading payors, and regulatory agencies.
Aetion brings deep scientific expertise and technology to life sciences, regulatory agencies (including FDA and EMA), payors, and health technology assessment (HTA) customers in the US, Canada, Europe, and Japan with analytics that can achieve the following:
Optimize clinical trials by identifying target populations, creating external control arms, and contextualizing settings and populations underrepresented in controlled settings
Expand industry access through label changes, pricing, coverage, and formulary decisions
Conduct safety and effectiveness studies for medications, treatments, and diagnostics
Aetion’s applications, including Discover and Aetion Substantiate, are powered by the Aetion Evidence Platform (AEP), a core longitudinal analytic engine capable of applying rigorous causal inference and statistical methods to hundreds of millions of patient journeys.
AetionAI is a set of generative AI capabilities embedded across the core environment and applications. Smart Subgroups Interpreter is an AetionAI feature in Discover.
The following figure illustrates the organization of Aetion’s services.
Smart Subgroups
For a user-specified patient population, the Smart Subgroups feature identifies clusters of patients with similar characteristics (for example, similar prevalence profiles of diagnoses, procedures, and therapies).
These subgroups are further classified and labeled by generative AI models based on each subgroup’s prevalent characteristics. For example, as shown in the following generated heat map, the first two Smart Subgroups within a population of patients who were prescribed GLP-1 agonists are labeled “Cataract and Retinal Disease” and “Inflammatory Skin Conditions,” respectively, to capture their defining characteristics.
After the subgroups are displayed, a user engages with AetionAI to probe further with inquiries expressed in natural language. The user can express questions about the subgroups, such as “What are the most common characteristics for patients in the cataract disorders subgroup?” As shown in the following screenshot, AetionAI responds to the user in natural language, citing relevant subgroup statistics in its response.
A user might also ask AetionAI detailed questions such as “Compare the prevalence of cardiovascular diseases or conditions among the ‘Dulaglutide’ group vs the overall population.” The following screenshot shows AetionAI’s response.
In this example, the insights enable the user to hypothesize that Dulaglutide patients might experience fewer circulatory signs and symptoms. They can explore this further in Aetion Substantiate to produce decision-grade evidence with causal inference to assess the effectiveness of Dulaglutide use in cardiovascular disease outcomes.
Solution overview
Smart Subgroups Interpreter combines elements of unsupervised machine learning with generative AI to uncover hidden patterns in real-world data. The following diagram illustrates the workflow.
Let’s review each step in detail:
Create the patient population – Users define a patient population using the Aetion Measure Library (AML) features. The AML feature store standardizes variable definitions using scientifically validated algorithms. The user selects the AML features that define the patient population for analysis.
Generate features for the patient population – The AEP computes over 1,000 AML features for each patient across various categories, such as diagnoses, therapies, and procedures.
Build clusters and summarize cluster features – The Smart Subgroups component trains a topic model using the patient features to determine the optimal number of clusters and assign patients to clusters. The prevalences of the most distinctive features within each cluster, as determined by a trained classification model, are used to describe the cluster characteristics.
Generate cluster names and answer user queries – A prompt engineering technique for Anthropic’s Claude 3 Haiku on Amazon Bedrock generates descriptive cluster names and answers user queries. Amazon Bedrock provides access to LLMs from a variety of model providers. Anthropic’s Claude 3 Haiku was selected as the model due to its speed and satisfactory intelligence level.
The solution uses Amazon Simple Storage Service (Amazon S3) and Amazon Aurora for data persistence and data exchange, and Amazon Bedrock with Anthropic’s Claude 3 Haiku models for cluster names generation. Discover and its transactional and batch applications are deployed and scaled on a Kubernetes on AWS cluster to optimize performance, user experience, and portability.
The following diagram illustrates the solution architecture.
The workflow includes the following steps:
Users create Smart Subgroups for their patient population of interest.
AEP uses real-world data and a custom query language to compute over 1,000 science-validated features for the user-selected population. The features are stored in Amazon S3 and encrypted with AWS Key Management Service (AWS KMS) for downstream use.
The Smart Subgroups component trains the clustering algorithm and summarizes the most important features of each cluster. The cluster feature summaries are stored in Amazon S3 and displayed as a heat map to the user. Smart Subgroups is deployed as a Kubernetes job and is run on demand.
Users interact with the Interpreter API microservice by using questions expressed in natural language to retrieve descriptive subgroup names. The data transmitted to the service is encrypted using Transport Layer Security 1.2 (TLS). The Interpreter API uses composite prompt engineering techniques with Anthropic’s Claude 3 Haiku to answer user queries:
Versioned prompt templates generate descriptive subgroup names and answer user queries.
AML features are added to the prompt template. For example, the description of the feature “Benign Ovarian Cyst” is expanded in a prompt to the LLM as “This measure covers different types of cysts that can form in or on a woman’s ovaries, including follicular cysts, corpus luteum cysts, endometriosis, and unspecified ovarian cysts.”
Lastly, the top feature prevalences of each subgroup are added to the prompt template. For example: “In Smart Subgroup 1 the relative prevalence of ‘Cornea and external disease (EYE001)’ is 30.32% In Smart Subgroup 1 the relative prevalence of ‘Glaucoma (EYE003)’ is 9.94%…”
Amazon Bedrock responds back to the application that displays the heat map to the user.
Outcomes
Smart Subgroups Interpreter enables users of the AEP who are unfamiliar with real-world data to discover patterns among patient populations using natural language queries. Users now can turn findings from such discoveries into hypotheses for further analyses across Aetion’s software to generate decision-grade evidence in a matter of minutes, as opposed to days, and without the need of support staff.
Conclusion
In this post, we demonstrated how Aetion uses Amazon Bedrock and other AWS services to help users uncover meaningful patterns within patient populations, even without prior expertise in real-world data. These discoveries lay the groundwork for deeper analysis within Aetion’s Evidence Platform, generating decision-grade evidence that drives smarter, data-informed outcomes.
As we continue expanding our generative AI capabilities, Aetion remains committed to enhancing user experiences and accelerating the journey from real-world data to real-world evidence.
With Amazon Bedrock, the future of innovation is at your fingertips. Explore Generative AI Application Builder on AWS to learn more about building generative AI capabilities to unlock new insights, build transformative solutions, and shape the future of healthcare today.
About the Authors
Javier Beltrán is a Senior Machine Learning Engineer at Aetion. His career has focused on natural language processing, and he has experience applying machine learning solutions to various domains, from healthcare to social media.
Ornela Xhelili is a Staff Machine Learning Architect at Aetion. Ornela specializes in natural language processing, predictive analytics, and MLOps, and holds a Master’s of Science in Statistics. Ornela has spent the past 8 years building AI/ML products for tech startups across various domains, including healthcare, finance, analytics, and ecommerce.
Prasidh Chhabri is a Product Manager at Aetion, leading the Aetion Evidence Platform, core analytics, and AI/ML capabilities. He has extensive experience building quantitative and statistical methods to solve problems in human health.
Mikhail Vaynshteyn is a Solutions Architect with Amazon Web Services. Mikhail works with healthcare life sciences customers and specializes in data analytics services. Mikhail has more than 20 years of industry experience covering a wide range of technologies and sectors.
