Conode for Healthcare

Knowledge Graphs reimagined for Health AI

A healthcare organization faced challenges extracting quantitative diagnostic results from unstructured doctors' notes.

Let’s take a look at how an AI Agent for medical feature extraction with >95% accuracy was built in Conode within just a few dev hours

Current GEN AI solutions for feature extraction are plagued with errors

Low accuracy (<80%) led to missed patients and errors.
High costs for tuning models and resources.
Data inconsistency required models to be rebuilt from scratch for every new dataset.

How Conode Fixed this problem

Automated knowledge graph creation from unstructured text reports

Extracted critical medical facts with a high-accuracy AI agent

Monitored and improved model performance across embedding spaces

Enhanced AI model accuracy with few-shot learning

Results

clip of the embedding getting better...?

In Conode, medical features were extracted from unstructured text with >95% accuracy in just a few dev hours:

Reduce Costs by reducing the need for humans in the loop
Improve Reliability by eliminating errors in reading patients' records
Increase Scalability across the business by keeping resources low

Additional use cases in Healthcare

Optimising Meal Uplifts based on Passenger Profiles

Identify flights with excessive meal uplifts and determine if socio- demographic profiles of passengers can predict food preferences, especially for perishable meals that contribute significantly to
food waste.

Having just scratched the surface in this session, we look forward to expanding our analysis by integrating additional data and scaling up our investigation to uncover deeper insights.

Examine Seasonal Demand

Evaluate customer preferences over time, focusing on how demand evolves with seasonality and flight schedules (e.g., morning vs. night flights).

Customer Experience

Map flight data with customer surveys to gain deeper visibility into customer satisfaction and identify opportunities to improve the experience on key routes.