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Updated: Jan 21, 2026
10:10
Long-term Culture of Human Breast Cancer Specimens and Their Analysis Using Optical Projection Tomography
Published on: July 29, 2011
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Learning Health System for Breast Cancer: Pilot Project Experience
Mark N Levine1,2, Gordon Alexander3, Arani Sathiyapalan1
1McMaster University, Hamilton, Ontario, Canada.
JCO Clinical Cancer Informatics
|August 2, 2019
Summary
Artificial intelligence (AI) and natural language processing (NLP) can extract valuable patient journey data from electronic health records (EHRs). This technology accurately characterizes clinical courses for stage III breast cancer patients.
Area of Science:
- Oncology
- Medical Informatics
- Artificial Intelligence
Background:
- Clinicians require timely treatment impact data.
- Electronic Health Records (EHRs) hold potential for real-world patient insights but are difficult to access.
- Characterizing complex clinical journeys is challenging.
Purpose of the Study:
- To apply AI to EHR data for characterizing the clinical course of stage III breast cancer patients.
- To demonstrate the feasibility of extracting and structuring patient journey data from EHRs.
- To validate the accuracy of AI-driven data extraction against gold-standard medical charts.
Main Methods:
- Extracted de-identified EHR data from 50 stage III breast cancer patients (2013-2015).
- Developed specialized Natural Language Processing (NLP) annotators to extract medical concepts from unstructured clinical text.
- Validated NLP annotators against medical charts for 19 patients, comparing 171 data elements.
Main Results:
- AI and NLP successfully extracted and structured patient journey data, creating timelines.
- Initial NLP agreement with gold standard charts was 76% for 171 data elements.
- After simple logic manipulation, disagreement reduced to 3.5%, with a high F1 statistic of 0.9694.
Conclusions:
- It is feasible to extract, read, and combine EHR data to visualize the patient journey.
- High agreement between NLP and gold standard charts supports the validity of this AI-driven approach.
- This method offers a promising way to gain insights into real-world patient outcomes.
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