Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research. This ultimately helps healthcare and life sciences researchers and practitioners create better insights from the data for a variety of use cases, such as drug discovery and personalized treatments.
In this post, we use Amazon HealthLake to export EHR data in the Fast Healthcare Interoperability Resources (FHIR) data format. We then build a knowledge graph based on key entities extracted and harmonized from the medical data. Amazon HealthLake also extracts and transforms unstructured medical data, such as medical notes, so it can be searched and analyzed. Together with Amazon Kendra and Amazon Neptune, we allow domain experts to ask a natural language question, surface the results and relevant documents, and show connected key entities such as treatments, inferred ICD-10 codes, medications, and more across records and documents. This allows for easy analysis of co-occurrence of key entities, co-morbidities analysis, and patient cohort analysis in an integrated solution. Combining effective search capabilities and data mining through graph networks reduces time and cost for users to find relevant information around patients and improve knowledge serviceability surrounding EHRs. The code base for this post is available on the GitHub repo.
In this post, we use the output from Amazon HealthLake for two purposes.
First, we index EHRs into Amazon Kendra for semantic and accurate document ranking out of patient notes, which help improve physician efficiency identifying patient notes and compare it with other patients sharing similar characteristics. This shifts from using a lexical search to a semantic search that introduces context around the query, which results in better search output (see the following screenshot).
Second, we use Neptune to build knowledge graph applications for users to view metadata associated with patient notes in a more simple and normalized view, which allows us to highlight the important characteristics stemmi
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/build-a-cognitive-search-and-a-health-knowledge-graph-using-amazon-healthlake-amazon-kendra-and-amazon-neptune/