Predicting schizophrenia at risk of readmissions in the short- and long-term using decision tree model
รหัสดีโอไอ
Creator Jaree Thongkam
Title Predicting schizophrenia at risk of readmissions in the short- and long-term using decision tree model
Contributor Vatinee Sukmak and Verayut Mayusiri
Publisher Research and Technology Transfer Affairs Division.Khon Kaen University
Publication Year 2559
Journal Title Asia-Pacific Journal of Science and Technology
Journal Vol. 21
Journal No. 3
Page no. 91-103
Keyword Psychiatric patient, Rehospitalization, Risk factors, Data mining
URL Website https://tci-thaijo.org/index.php/APST/index
Website title https://tci-thaijo.org/index.php/APST/article/view/69553
ISSN 2539-6293
Abstract This study aims to develop readmission prediction models using a decision tree technique in data mining for predicting patients with schizophrenia at risk of readmission for four different time periods after discharge: ? 6 months, 6-12 months, 1-2years, > 2years. Information on the socio-demographic and clinical characteristics data were collected from the registered medical files of patients. Of the 2,285 patients admitted to Prasrimahabhodi Psychiatric Hospital between January 2007 and December 2012, 778 (34.05%) were read-missions. Almost 30% of these patients were readmitted within 6 months of discharge. The non-compliance with medication patients who were diagnoses of F20.3, F20.5 and F20.8 tend to be readmitted within 6 month, while subtype diagnoses of F20.1, F20.2 and F20.4 tend to be readmitted between 6 months and 1 year. Fur-thermore, patients who were subtype diagnoses of F20.2, F20.3, F20.4, F20.5 and F20.8 tend to be readmitted after 2 years. Among the patients who had low compliance to medication with diagnoses of F20.0 and F20.1 if they are unmarried, widowed and divorced, they tend to be readmitted after 2 years. The experimental results also showed that schizophrenia readmission prediction model achieved the highest accuracy, true positive rate, and true negative rate of short-term readmission up to 93.38%, 94.07% and 92.68%, and long-term read-mission up to 97.40%, 98.05% and 96.44%, respectively. The implications of this study may help to increase our understanding of early intervention and will enable clinicians and practitioners in planning care.
Asia-Pacific Journal of Science and Technology

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