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Automated plan-recognition of chemotherapy protocols.
1Vanderbilt University School of Medicine, Department of Biomedical Informatics, Nashville, TN, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|December 24, 2011
Summary
A new data-driven method accurately identifies sequential chemotherapy protocols from pharmacy records, simplifying cancer treatment plan recognition for clinicians. This approach aids in understanding complex patient treatment histories.
Area of Science:
- Oncology
- Health Informatics
- Data Science
Background:
- Cancer treatment often involves complex, sequential chemotherapy protocols.
- Current clinical documentation struggles to efficiently capture treatment sequences.
- Accurate abstraction of chemotherapy treatment plans is crucial for patient care.
Purpose of the Study:
- To develop and evaluate a data-driven method for recognizing cancer treatment plans.
- To automate the abstraction of sequential chemotherapy protocols from dispensing records.
Main Methods:
- Utilized pharmacy chemotherapy dispensing records as input.
- Developed a data-driven algorithm for cancer treatment plan recognition.
- Validated the method against a manually annotated gold standard.
Main Results:
- Achieved 75% accuracy and 80% precision for breast cancer data (110 patients, 2,029 events).
- Achieved 54% accuracy and 63% precision for lung cancer data (53 patients, 670 events).
Conclusions:
- The developed method effectively recognizes sequential chemotherapy protocols.
- This approach can provide clinicians and systems with an abstracted view of patient treatment history.
- Potential to improve the management and understanding of complex cancer therapies.
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