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Patient journey through cases of depression from claims database using machine learning algorithms.

Yoshitake Kitanishi1, Masakazu Fujiwara1, Bruce Binkowitz2

  • 1Data Science Office, Shionogi & Co. Ltd., Osaka, Japan.

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|February 16, 2021
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Summary

Machine learning and Topological Data Analysis (TDA) Mapper were used to visualize patient journeys for depression diagnoses. This approach offers a visual classification of diseases associated with depression, aiding precision medicine.

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Area of Science:

  • Health Informatics
  • Computational Biology
  • Data Science

Background:

  • Real-world data from health insurance and hospital claims are increasingly available in Japan.
  • Machine learning methods are being explored for analyzing this data, but methodologies for visualizing patient journeys remain under development.
  • Existing machine learning approaches estimate data correlation structures for disease classification and prognosis prediction.

Purpose of the Study:

  • To apply association analysis to real-world data to define patient journeys for depression diagnoses.
  • To address the limitations of association analysis in simultaneously interpreting multiple outcome measures.
  • To utilize Topological Data Analysis (TDA) Mapper for sequential interpretation of multiple indices and visual classification of depression-associated diseases.

Main Methods:

  • Applied association analysis to real-world health insurance and hospital claims data to model patient journeys for depression.
  • Employed Topological Data Analysis (TDA) Mapper to interpret multiple indices and visualize disease associations sequentially.
  • Focused on a cohort with depression diagnoses.

Main Results:

  • Developed a visual and continuous classification of diseases commonly associated with depression using TDA Mapper.
  • Demonstrated the utility of TDA Mapper in overcoming the interpretability challenges of traditional association analysis for complex health data.
  • Identified patterns in patient journeys related to depression diagnoses.

Conclusions:

  • The TDA Mapper approach provides a novel method for visualizing and understanding complex disease associations within patient journeys.
  • This visual classification can significantly contribute to precision medicine research by identifying comorbidities and informing personalized treatment strategies.
  • The findings support the use of advanced data analysis techniques for leveraging real-world health data to improve patient care and pharmaceutical research.