Once we created this knowledge graph, we experimented with the different graph algorithms.
Step 2: Build personalized Pagerank model using GraphSage and Optuna optimization
- Based on the clinical knowledge graph created in Neo4j, the baseline Pagerank model was built.
- In order to consider the node attributes, the node embeddings were used to represent the similar nodes with nearby vectors and the kNN algorithm was used to connect the top 10 most similar admissions.
- Lower weights were given to the edges of popular nodes based on their degree in order to reduce their popularity effect.
- Next, a GraphSage model was trained to create the neighborhood embeddings of the nodes present in the bipartite knowledge graph.
- For optimizing the weights of the edges, Optuna was used.
Step 3: MedLM augmented results
MedLM harnesses the power of Google’s MedLM, and is aligned to the medical domain to more accurately answer medical questions. It can be used to facilitate rich, informative discussions, answer complex medical questions, and find insights in complicated and unstructured medical texts. It is also used to help draft short- and long-form responses and summarize documentation and insights from internal data sets and bodies of scientific knowledge.
In our experiments, we fed the output of the Personalized Pagerank model to MedLM as context and generated the final response, which had a higher accuracy.
We observed that the final responses generated by this approach using MedLM and clinical knowledge graphs were grounded in factuality and are far more accurate by reducing the false positives and boosting the true positives.
“This solution built on MedLM augmented with a clinical knowledge graph can analyze a patient’s medical records and generate insights on relevant medications, laboratory evaluations, medical procedures, and potential diagnoses for the clinician to review. By generating these evidence-based insights, this gen AI solution aims to enhance the clinical workflows, reduce errors, and improve patient outcomes. And it is super important to understand that this is just the tip of the iceberg in terms of the AI’s capabilities where it is so powerful, but yet always assistive to the clinicians.” – Abdussamad M, Engineering Lead at Apollo 24|7.
This solution is not intended to replace the clinician’s expertise but rather to augment the clinician’s skills and experience.
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The partnership between Google Cloud and Apollo 24|7 is just one of the latest examples of how we’re providing AI-powered solutions to solve complex problems to help organizations drive the desired outcomes. With Google Cloud Consulting (GCC), Apollo was able to perform repeated iterations and experiments to build the final solution, thereby empowering the business. Apollo entrusted GCC to collaborate with their teams to build the state of the workflows for their business requirements. The GCC portfolio provides a unified services capability, bringing together offerings across multiple specializations, into a single place. This includes services from learning to technical account management to professional services and customer success. See Google Cloud Consulting’s full portfolio of offerings.
Disclaimer: MedLM is still in the preview phase in India and it is not approved for production use
