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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
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.
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.
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