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A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal

Kelly Voigt1, Yingtao Sun2, Ayush Patandin3

  • 1Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus Medical Centre Cancer Institute, Rotterdam, Netherlands.

JMIR Cancer
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PubMed
Summary

Machine learning analyzed patient forum data to understand colorectal cancer (CRC) experiences. This approach revealed key concerns and emotional impacts, especially in home settings, aiding better patient support.

Keywords:
AIUnited Statesartificial intelligencecancer carecancer survivorcolorectal cancerforummachine learningpatient experiencepatient journeypostquality of lifetopictopic modeling

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Area of Science:

  • Health Informatics
  • Computational Social Science
  • Oncology Patient Experience Research

Background:

  • Increasing cancer survivor numbers and healthcare professional shortages strain cancer care accessibility.
  • Health technologies are vital for managing patient journeys, yet qualitative insights are often limited by patient willingness to share.
  • Understanding the daily lives of patients throughout their cancer journey requires innovative data analysis methods.

Purpose of the Study:

  • To identify and assess patient experiences on a large scale using a novel machine learning approach.
  • To leverage data from patient forums to gain insights into the patient journey.
  • To develop a patient community journey map based on identified experiences.

Main Methods:

  • Utilized 212,107 posts from colorectal cancer (CRC) patients on the Cancer Survivors Network USA forum.
  • Applied machine learning topic modeling to identify patterns in forum discussions.
  • Analyzed posts categorized by home and hospital contexts, with expert evaluation of findings and a developed patient community journey map.

Main Results:

  • Identified 37 topics and 10 clusters from forum data, with 'Daily activities while living with CRC' and 'Understanding treatment' being dominant.
  • Discussions within the home context exhibited significantly more emotional content than those in the hospital context.
  • A patient community journey map was constructed, visually representing the diverse experiences and challenges faced by CRC patients.

Conclusions:

  • Machine learning-supported analysis of patient forums offers a promising method for understanding diverse patient experiences at scale.
  • The heightened emotional content in home-based discussions highlights the profound personal impact of CRC beyond clinical settings.
  • Patient community journey mapping provides valuable insights into daily challenges, crucial for timely and appropriate patient support.