ArticleBMC medical informatics and decision making2017
Quad-phased data mining modeling for dementia diagnosis.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.Journal of medical systems · 2018Pooled it
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
- Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.Scientific reports · 2022Article
- Development and validation of new screening tool for predicting dementia risk in community-dwelling older Japanese adults.Journal of translational medicine · 2021Article
- A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining.Diagnostics (Basel, Switzerland) · 2021Review
- Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery.BMC medical informatics and decision making · 2020Article
- Dementia Patient Segmentation Using EMR Data Visualization: A Design Study.International journal of environmental research and public health · 2019Article
- Predictors of intentional intoxication using decision tree modeling analysis: a retrospective study.Clinical and experimental emergency medicine · 2018Article
- Evolution of Translational Bioinformatics: lessons learned from TBC 2016.BMC medical genomics · 2017Article
- The characteristics and performance of health data domains in supporting dementia identification: A systematic review.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians.
methodsTherefore, in this paper we propose a quad-phased data mining modeling consisting of 4 modules. In Proposer Module, significant diagnostic criteria are selected that are effective for diagnostics. Then in Predictor Module, a model is constructed to predict and diagnose dementia based on a machine learning algorism. To help clinical physicians understand results of the predictive model better, in Descriptor Module, we interpret causes of diagnostics by profiling patient groups. Lastly, in Visualization Module, we provide visualization to effectively explore characteristics of patient groups.
resultsThe proposed model is applied for CREDOS study which contains clinical data collected from 37 university-affiliated hospitals in republic of Korea from year 2005 to 2013.
conclusionsThis research is an intelligent system enabling intuitive collaboration between CAD system and physicians. And also, improved evaluation process is able to effectively reduce time and cost consuming for clinicians and patients.
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