Type of Document Master's Thesis Author Bhatia, Haresh Lokumal Author's Email Address email@example.com URN etd-06122012-142536 Title Chemotherapy Plan Abstraction Method Degree Master of Science Department Biomedical Informatics Advisory Committee
Advisor Name Title Mia Levy Committee Chair Josh Denny Committee Member Tom Lasko Committee Member Keywords
- Event Truthfulness and Accuracy
- Medication Events
- Temporal Abstraction
- Temporal Reasoning
- Clinical Plan
- Plan Recognition
- Data-driven Method
Date of Defense 2011-12-01 Availability unrestricted AbstractPurpose: Chemotherapy plan abstraction is an important clinical and research task in medicine. Providers review the treatment plan history and the response to the treatment to make decisions about continuing or changing the current treatment. Likewise, medical researchers want to know the treatment plan history for a cohort of the patients under analysis. It is difficult for providers and researchers to efficiently abstract the sequence and nature of treatment plans from discrete drug events, as recorded by the clinical documentation procedures. I hypothesize that an automated plan abstraction method can accurately abstract medication plans from the temporal sequence of medication event records across multiple cancer domains.
Methods: I have developed a data-driven plan abstraction method that takes as input pharmacy chemotherapy dispensing records and produces a sequence of chemotherapy plans. The framework consists of a pre-processing method, the plan abstraction method, and cohort analysis. The performance of the method was tested against a manually annotated gold standard set of chemotherapy plans. The method was first trained and tested on a data set limited to breast cancer and lung cancer patients. The generalizability of the method was then tested on a separate data set that includes all solid tumor cancer diagnoses other than breast and lung cancer. The method’s utility was then demonstrated for cohort plan analysis using a data set of medication events from a large breast cancer cohort. Across plan and within plan analyses were performed on the treatment plan history obtained by applying the method to this breast cancer cohort.
Results: For performance evaluation, the plan abstraction method was tested on a sample of 341 breast cancer and lung cancer patients with 6,050 chemotherapy medication events, and a sample of 168 non-breast cancer and non-lung cancer patients with solid tumors who had 3,366 chemotherapy medication events. For these two sets, the recall rate was 0.913 and 0.899, and the precision rate was 0.768 and 0.696, respectively. Treatment plan analysis was performed on a separate breast cancer cohort of 554 patients. This cohort consisted of 11,789 chemotherapy medication events, with 1,126 overall total plans and 107 unique plans identified. The analysis of the 5 most frequently prescribed plans shows concordance with national guideline recommendations for plan sequencing, cycle frequency, and number of cycles. A separate analysis of the breast cancer chemotherapeutic the fulvestrant plan for metastatic cancer showed concurrence to published results for the median time to disease progression in a randomized clinical trial.
Conclusion: The plan abstraction method can accurately produce a well-structured sequence of chemotherapy plans from individual drug events across multiple cancer domains. Furthermore, I have demonstrated the utility of this method for cohort analysis of a large data set. I believe this plan abstraction method could become an important tool for clinicians and researchers to automatically extract chemotherapy treatment history from electronic health record data sources.
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