Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.

Journal: Studies in health technology and informatics
Published Date: Aug 22, 2024

Abstract

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset. This work highlights the importance of temporal nuances in healthcare data analysis, paving the way for improved disease understanding and patient care.

Authors

  • Jonas Hügel
    Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany.
  • Donata A Schäfer
    University Medical Center Göttingen, Department of Hematology and Medical Oncology, Göttingen, Germany.
  • Jan J Schneider
    University Medical Center Göttingen, Department of Medical Informatics, Göttingen, Germany.
  • Jiazi Tian
    Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Hossein Estiri
    Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Partners Healthcare, Somerville, MA, USA. Electronic address: hestiri@hms.harvard.edu.
  • Raphael Koch
    University Medical Center Göttingen, Department of Hematology and Medical Oncology, Göttingen, Germany.
  • Tobias R Overbeck
    University Medical Center Göttingen, Department of Hematology and Medical Oncology, Göttingen, Germany.
  • Ulrich Sax
    Department of Medical Informatics, University Medical Center Goettingen, Goettingen, Germany.

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