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Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients...Medical Informatics Europe (MIE) 34th Conference, August 25-29, 2024, Athens, Greece.

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 vi...

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Published in:Studies in Health Technology & Informatics Vol. 316; pp. 1642 - 1647
Main Authors: HÜGEL, Jonas, SCHÄFER, Donata A., SCHNEIDER, Jan J., Jiazi TIAN, ESTIRI, Hossein, KOCH, Raphael, OVERBECK, Tobias R., SAX, Ulrich
Format: proceedings research tables/charts Journal Article
Published: Sage Publications Inc. 2024
Online Access:View this record in EBSCOhost
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      dt: 2024
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      pub: Sage Publications Inc.
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        atl: Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients...Medical Informatics Europe (MIE) 34th Conference, August 25-29, 2024, Athens, Greece.
      aug:
        au:
          HÜGEL, Jonas
          SCHÄFER, Donata A.
          SCHNEIDER, Jan J.
          Jiazi TIAN
          ESTIRI, Hossein
          KOCH, Raphael
          OVERBECK, Tobias R.
          SAX, Ulrich
        affil: University Medical Center Göttingen,Department of Medical Informatics, Göttingen, Germany.2.
      sug:
        subj:
          Lung Neoplasms Therapy
          Workflow Evaluation
          Machine Learning
          Algorithms Evaluation
          Patient Care
          Treatment Outcomes
          Congresses and Conferences Greece
          Greece
          Human
          Funding Source
          Data Analysis
          Data Mining
          Electronic Health Records
          Health Informatics
          Data Analysis Software
      ab: 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.
      pubtype: Academic Journal
      doctype:
        proceedings
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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