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Related Experiment Video

Updated: Mar 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Transitioning to a Data Driven Mental Health Practice: Collaborative Expert Sessions for Knowledge and Hypothesis

Vincent Menger1, Marco Spruit1, Karin Hagoort2

  • 1Department of Information and Computing Sciences, Utrecht University, P.O. Box 80089, 3508 TB Utrecht, Netherlands.

Computational and Mathematical Methods in Medicine
|September 16, 2016
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Summary

Healthcare data analysis can uncover new knowledge. The proposed Cross Industry Standard Process for Data Mining - Interactive Data Mining (CRISP-IDM) method effectively generates novel hypotheses by collaborating with healthcare professionals.

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Area of Science:

  • Health Informatics
  • Data Mining
  • Psychiatry Research

Background:

  • Increasing healthcare data volume necessitates novel research approaches.
  • Traditional data analysis may limit discovery of unexpected insights.

Purpose of the Study:

  • To propose and evaluate CRISP-IDM for collaborative knowledge discovery in healthcare.
  • To leverage expert sessions and data visualization for hypothesis generation.

Main Methods:

  • Adaptation of the Cross Industry Standard Process for Data Mining (CRISP-DM) into CRISP-IDM.
  • Case study at University Medical Center Utrecht's psychiatry department.
  • Expert interviews and data visualization for collaborative analysis with healthcare professionals.

Main Results:

  • Identified seven research themes through expert interviews.
  • Conducted 19 expert sessions, yielding two implemented results and 29 new research hypotheses.
  • 24 of the 29 hypotheses were unanticipated during initial interviews.

Conclusions:

  • CRISP-IDM demonstrates viability for uncovering new knowledge and hypotheses in healthcare.
  • Involving healthcare professionals in data analysis enhances discovery.
  • Data visualization is an effective tool for collaborative modeling and hypothesis generation.