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Developing a computable phenotype for glioblastoma.

Sandra Yan1, Kaitlyn Melnick1, Xing He2,3

  • 1Department of Neurosurgery, College of Medicine, University of Florida, Gainesville, Florida, USA.

Neuro-Oncology
|December 23, 2023
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Summary

Researchers developed a computable phenotype (CP) to accurately identify glioblastoma multiforme (GBM) patients in electronic health records (EHRs). This method combines structured and unstructured data, improving patient identification for population studies.

Keywords:
Electronic Health Records (EHRs)computable phenotypeglioblastomastructured dataunstructured data

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Area of Science:

  • Medical Informatics
  • Oncology
  • Data Science

Background:

  • Glioblastoma is the most common malignant brain tumor.
  • Accurate patient identification is crucial for population studies.
  • Diagnostic codes for glioblastoma are often nonspecific, posing challenges for data extraction.

Purpose of the Study:

  • To develop a computable phenotype (CP) for glioblastoma multiforme (GBM).
  • To utilize both structured and unstructured data from electronic health records (EHRs).
  • To improve the identification of GBM patients for research purposes.

Main Methods:

  • Utilized the University of Florida Health Integrated Data Repository.
  • Performed iterative refinement of diagnosis codes, procedure codes, medication codes, and keywords through manual chart review.
  • Evaluated multiple proposed CPs to determine the best-performing algorithm based on F1-score.

Main Results:

  • Six rounds of manual chart reviews were conducted to refine CP elements.
  • The optimal CP rule combined relevant diagnosis codes and keywords.
  • The selected CP rule demonstrated the highest F1-score using both structured and unstructured data.

Conclusions:

  • A validated CP algorithm for identifying GBM patients was developed.
  • The algorithm effectively uses structured and unstructured EHR data from a large tertiary care center.
  • The final algorithm achieved a high F1-score of 0.817, minimizing misclassification errors.