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Table 1 Conceptual differences of data mining approach.

From: Data mining of mental health issues of non-bone marrow donor siblings

Research area Electronic medical record Genomics/Proteomics This study: Mental health on NDS
Data source Physicians/nurses' Description, laboratory data and radiologic images on medical record Gene expression data from cDNA microarray/mass spectrometry Interview with the subject
Expected results Automatic and effective data extraction/sorting Extraction of genes/proteins with statistical significance
Classification of gene/proteins
Visualization of gene/protein expression pattern or pathway
Extraction of important and rarely-appeared words
Visualization of relationship between keywords
Concept* Supervised/Unsupervised approach Supervised/Unsupervised approach Unsupervised approach
Representative algorism of data mining technique Data extraction matching with prepared data criteria To provide statistically meaningful analysis for high-throughput and multi-dimensional biological data in the association with phenotype To discover unanticipated, rarely appeared key-elements by Scenario Map analysis
Aims Linking between medical record description and research issues
To develop effective and commonly available electronic health record
To discover new biomarker or diagnostic method
To discover therapeutic target
For better clinical follow-up by understanding unanticipated individual concerns
  1. Conceptual differences of data mining approach in representative medical research areas are shown. *Supervised approach aims for testing or validation of hypothesis while unsupervised approach used for discovering unanticipated events or knowledge.