Clinical Ontology Tokenization
Clinical Ontology Tokenization and Coding with LLMs
Objective:
This lab introduces students to the importance of clinical ontologies (e.g., SNOMED CT, ICD-10, LOINC, CPT) and their role in ensuring semantic interoperability in healthcare systems. Students will use a Large Language Model (LLM) to process a clinical note, extract clinical concepts, map them to the appropriate ontology codes, and identify medications. The exercise emphasizes the need for accurate tokenization of clinical data and understanding ontology standards to improve healthcare workflows and analytics.
Lab Overview
Introduction to Ontologies and Tokenization:
Understand the role of clinical ontologies in standardizing healthcare data for interoperability and analytics.
Explore the importance of accurate tokenization in extracting meaningful concepts from unstructured clinical notes.
Overview of Coding Systems:
Learn about key coding systems used in healthcare:
SNOMED CT: Standardized clinical terminology.
ICD-10: International classification of diseases.
LOINC: Standardized coding for lab tests and observations.
CPT: Codes for medical procedures and services.
RxNorm: Codes for medications.
Processing Clinical Notes with LLMs:
Use a Large Language Model to tokenize and extract clinical concepts from unstructured text.
Map the extracted concepts to the appropriate ontology codes.
Extracting Medications:
Identify medications mentioned in the clinical note.
Extract details such as drug name, dosage, form, route and administration instructions.
Validation and Reporting:
Validate the accuracy of the extracted concepts and ontology codes.
Reflect on the challenges and importance of accurate coding in healthcare data management.
Lab Workflow
Setup:
Review the sample ontology mappings for practice.
Tokenization and Extraction:
Use the LLM to process the clinical note.
Extract key clinical concepts (e.g., diagnoses, symptoms, lab results).
Ontology Mapping:
Match extracted concepts to the appropriate coding system:
For diagnoses: Use ICD-10 and SNOMED CT.
For labs: Use LOINC.
For procedures: Use CPT
For Medications use RxNorm
Validation:
Cross-check the mappings against provided examples or databases (e.g., SNOMED CT browser, ICD-10 lookup tools).
Correct any discrepancies or misclassifications.
Reporting:
Summarize the extracted clinical concepts, ontology mappings, and medications in a structured report.
Reflect on the role of LLMs in automating this process and their limitations.
Learning Objectives
By the end of this lab, students will be able to:
Understand Clinical Ontologies:
Explain the need for standardized clinical concepts and coding systems in healthcare.
Describe the purpose and application of SNOMED CT, ICD-10, LOINC, CPT, and RxNorm codes.
Tokenize Clinical Notes:
Use LLMs to extract clinical concepts from unstructured text.
Understand the challenges of tokenizing complex medical language.
Map Concepts to Ontologies:
Accurately map extracted clinical concepts to appropriate coding systems.
Explain the implications of incorrect or incomplete ontology mappings.
Validate and Troubleshoot Mappings:
Validate ontology mappings using reference tools and correct discrepancies.
Understand the importance of accuracy in clinical coding for analytics and interoperability.
Appreciate the Role of LLMs in Healthcare:
Discuss the advantages and limitations of using LLMs for clinical data processing.
Reflect on the future potential of AI in automating healthcare workflows.
Outcome
This lab provides students with hands-on experience in leveraging AI tools like LLMs to extract and standardize clinical data. It emphasizes the importance of accurate ontology coding in healthcare and prepares students for real-world applications where structured, interoperable data is critical for patient care and system integration.
Last updated