Transforming Healthcare with AI-Powered Medical Feature Extraction: A Success Story

shona060
Jan 8
2 min read

Updated: Jan 12

Challenge

A Healthcare organization was struggling to extract quantitative diagnostic results from their unstructured doctors' notes. Some of the hurdles they faced included:

Low accuracy: Existing NLP solutions delivered less than 80% accuracy, resulting in missed diagnoses and errors.
High costs: Tuning models and allocating resources proved expensive.
Data inconsistency: Every new dataset required rebuilding models from scratch, adding time and complexity.

Key Results

In Conode, medical features were extracted from unstructured clinical data with >95% accuracy in just a few dev hours. This enabled us to:

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.

The results were nothing short of transformative. Let’s take a look at how we tackled this project with Conode...

1. Automated Knowledge Graph Creation

With a single-click, data is uploaded and Conode automatically transforms the unstructured text from doctors' notes into a unified knowledge graph. This step provided a structured and interpretable representation of the data, made fully observable and queryable through the Conode platform.

2. Medical Feature Extraction

Armed with Conode’s Extract AI Agent, the team began extracting the key diagnostic results from the clinical note documents, through nothing but natural language. The observability provided by the Conode platform allowed the team to quickly and easily assess the results of the feature extraction task. For example, we ask for a confidence score alongside the results, then simply reach out and select the the clinical notes which are current failure modes of the model and hence might require more attention.

3. Performance Monitoring Across Embedding Spaces

Conode has always been the go-to platform for monitoring model performance across high dimensional learning spaces. Here we plot the clinical notes in an embedding to quickly identify areas of poor accuracy and address them efficiently. Repeating this process resulted in improved model performance within about an hour’s work.

4. Few-Shot Learning for Rapid Improvement

Finally, the team leveraged few-shot learning to improve performance by training the feature extraction model on just a handful of examples. This approach accelerated accuracy improvements across the entire knowledge graph without requiring extensive retraining.

Note that this end-to-end process required no coding, making it accessible to users with minimal technical expertise.

The Impact: Better Outcomes in Hours, Not Months

With Conode, a medical feature extraction AI agent with >95% accuracy was built in just a few development hours.

Cost Reduction: Automation minimized the need for human intervention, significantly lowering operational costs.
Improved Reliability: Errors in reading patient records were virtually eliminated, ensuring more accurate diagnostics.
Scalability: The streamlined process enabled easy adaptation to new datasets and business needs, making the solution for transforming unstructured data into actionable insights highly scalable.

This success story highlights the power of AI-driven platforms like Conode to revolutionize healthcare operations. By addressing challenges of accuracy, cost, and scalability, Conode has set a new standard for extracting critical information from unstructured medical data—all with unparalleled efficiency and ease.

Are you ready to harness the power of AI Knowledge Graphs?

With Conode, the future of medical feature extraction is just a few hours away.

Get in touch info@conode.ai.