• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.217-225
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• 2011

It is important to find risk factors associated with mental disorder. Also the hazard ratio that represent the relationship of risk factors with illness is main interest in medicine. Thus we used odds ratio to explore the relationship between mental disorder and risk factors. On this paper, when we applied Bayesian network to delirium of mental disorder, we selected major risk factors and calculated odds ratio. Especially we identified odds ratio of single risk factors and multiple risk factors.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.323-333
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• 2011

We analyzed using logistic to find factors with a mental disorder because logistic is the most efficient way assess risk factors. In this paper, we applied data mining techniques that are logistic, neural network, c5.0, cart and Bayesian network to delirium data. The Bayesian network method was chosen as the best model. When delirium data were applied to the Bayesian network, we determined the risk factors associated with delirium as well as identified the network between the risk factors.

• Korean Journal of Psychosomatic Medicine
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• v.7 no.2
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• pp.241-246
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• 1999

Delirium is a syndrome characterized by impairement of consciousness, disorientation, disturbance of sleep-wake cycle, memory impairement, disturbance of perception. It is induced by many causes, which are CNS diseases(head trauma, vascular disease, brain tumor, etc), medical diseases(metabolic disorder, endocrine disturbance, cardiovascular disease) and drugs(anticholinergics, anticonvulsant, antipsychotics, cimetidine etc). Transdermal scopolamine which is usually used to prevent motion sickness has anticholinergic property, and so it can induce delirium. The authors report two cases of delirium induced by transdermal scopolamine. The cases shared common characteristics which were as follows : 1. All of two patients were elderly women. 2. Delirium symptom was abruptly occurred during trip after attaching scopolamine patches. 3. Delirium symptom was rapidly improved within 2-3 days. It is important to educate for both users and managers about directions for transdermal scopolamine patch usage to prevent delirium. And careful history taking is needed to diagnose delirium induced by transdermal scopolamine accurately.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.911-919
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• 2011

In recent medical analysis, it becomes more important to looking for risk factors related to mental illness. If we find and identify their relevant characteristics of the risk factors, the disease can be prevented in advance. Moreover, the study can be helpful to medical development. These kinds of studies of risk factors for mental illness have mainly been discussed by using the logistic regression model. However in this paper, data mining techniques such as CART, C5.0, logistic, neural networks and Bayesian network were used to search for the risk factors. The Bayesian network of the above data mining methods was selected as most optimal model by applying delirium data. Then, Bayesian network analysis was used to find risk factors and the relationship between the risk factors are identified through a radial graph.

• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.37-44
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• 2001

Background: Sleep disorders are prevalent in the general population and in medical practice. Three diagnostic classifications for sleep disorders have been developed recently: The International Classification of Sleep Disorders (ICSD), The Diagnostic and Statistical Manual, 4th edition (DSM-IV) and The International Classification of Diseases, 10th edition (ICD-10). Few data have yet been published regarding how the diagnostic systems are related to each other. To address these issues, we evaluated the frequency of sleep disorder diagnoses by DSM-IV and ICSD and compared the DSM-IV with the ICSD diagnoses. Method: Two interviewers assessed 284 inpatients who had been referred for sleep problems in general units of Anam Hospital, holding an unstructured clinical interview with each patient and assigning clinical diagnoses using ICSD and DSM-IV classifications. Results: The most frequent DSM-IV primary diagnoses were "insomnia related to another mental disorder (61.1% of cases)" and "delirium due to general medical condition (26.8%)". "Sleep disorder associated with neurologic disorder (38.4% of cases)" was the most frequent ICSD primary diagnosis, followed by "sleep disorder associated with mental disorder (33.1%)". In comparing the DSM-IV diagnoses with the ICSD diagnoses, sleep disorder unrelated with general medical condition or another mental disorder in DSM-IV categories corresponded with these in ICSD categories. But DSM-IV "primary insomnia" fell into two major categories of ICSD, "psychophysiologic insomni" and "inadequate sleep hygiene". Of 269 subjects, 62 diagnosed with DSM-IV sleep disorder related to general medical condition or another mental disorder disagreed with ICSD diagnoses, which were sleep disorders not associated with general medical condition or mental disorder, i. e., "inadequate sleep hygiene", "environmental sleep disorder", "adjustment sleep disorder" and "insufficient sleep disorder". Conclusion: In this study, we found not only a similar pattern between DSM-IV and ICSD diagnoses but also disagreements, which should not be overlooked by clinicians and resulted from various degrees of understanding of the pathophysiology of the sleep disorders among clinicians. Non-diagnosis or mis-diagnosis leas to inappropriate treatment, therefore the clinicians' understanding of the classification and pathophysiology of sleep disorders is important.