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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Bibliographic Details
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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Summary:	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.