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Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data.

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  • 1INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.

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|August 24, 2018
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Summary

Inductive Logic Programming (ILP) offers a powerful method for analyzing complex clinical trial data, revealing diverse patient improvement patterns in blended Internet-based depression treatments. This approach helps identify predictors of treatment success by mining multi-relational data effectively.

Keywords:
Ecological momentary assessmentInternet interventionLog dataMoodbusterMulti-relational data mining

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Area of Science:

  • Computational psychiatry
  • Data mining in healthcare
  • Machine learning for clinical decision support

Background:

  • Blended Internet-based treatments generate complex, multi-source data (questionnaires, logs, EMA).
  • Mining this data for clinically relevant patterns is challenging.
  • Existing methods lack a definitive approach for complex relational data analysis.

Purpose of the Study:

  • To explore the utility of multi-relational Inductive Logic Programming (ILP) for analyzing complex clinical trial data.
  • To apply ILP to combined data from the EU E-COMPARED depression trial.
  • To identify clinically meaningful predictors of system use and treatment outcome.

Main Methods:

  • Utilized Inductive Logic Programming (ILP) for its ability to handle multi-relational data with temporal reasoning.
  • Analyzed data including PHQ-8 depression scores and Ecological Momentary Assessments (EMA) of mood.
  • Integrated self-report, EMA, and platform log data from the E-COMPARED trial.

Main Results:

  • Identified diverse individual improvement trajectories in depression treatment.
  • Observed variations in symptom severity (PHQ-8 scores) over the 9-16 week treatment period.
  • Aimed to uncover potential causes for differing treatment outcomes by combining various data sources.

Conclusions:

  • ILP demonstrates potential for analyzing complex, multi-relational clinical data.
  • The approach can yield comprehensible models for domain experts.
  • This study contributes to exploring alternative data mining techniques for E-COMPARED trial data.