PREDICTIVE FACTORS OF READMISSION IN PATIENTS WITH SCHIZOPHRENIA USING DATA MINING TECHNIQUES
Keywords:
readmission, schizophrenia, predictive factors, data miningAbstract
Objectives: To determine predicting the associated factors of relapse in patients with schizophrenia by using data mining with decision trees.
Material and Methods: The retrospective study design by data mining in the BMS-HOSxP program, Somdet Chaopraya Institute of Psychiatry was conducted. The samples consisted of 3,549 schizophrenic patients who returned to admission in Somdet Chaopraya Institute of Psychiatry from 1st January 2016 to 3oth December 2018. The factors of general data were taken from HOSxP database (Bangkok Medical Software-Hospital information extreme platform): gender, age, length of hospitalization, substance using, follow-up visiting, area health board, syrup antipsychotic drug using, long-acting injectable antipsychotics drug using and readmission date. Approach method of analysis used in this research was decision tree model.
Results: The finding revealed that majors predictive factors of readmission in schizophrenic patients were continuous follow-up appointment, liquid antipsychotics, and antipsychotic long acting Injections using. Data mining techniques was able to be used to predict hospital readmission the best accuracy of 61.5%, while 73.1% and 51.5% were the accuracy of admission date within 6 months and more than 6 months, respectively.
Conclusion: Adherence to prevention of rehospitalization in schizophrenia are important and could be improved if continuous follow-up appointment, liquid antipsychotics, and antipsychotic long acting Injections using were continually encouraging.
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