Extract critical factors affecting the length of hospital stay of pneumonia patient by data mining (case study: an Iranian hospital)

Publication date: Available online 13 July 2017
Source:Artificial Intelligence in Medicine
Author(s): Naghmeh Khajehali, Somayeh Alizadeh
MotivationPneumonia is a prevalent infection of lower respiratory tract caused by infected lungs. Length of stay (LOS) in hospital is one of the simplest and most important indicators in hospital activity that is used for different purposes. The aim of this study is to explore the important factors affecting the LOS of patients with pneumonia in hospitals.MethodsThe clinical data set for the study were collected from 387 patients in a specialized hospital in Iran between 2009 and 2015. Patients discharge summary includes their demographic details, reasons for admission, prescribed medications for the patient, the result of laboratory tests, and length of treatment.Results and conclusionsThe proposed model in the study demonstrates the way various scenarios of data processing impact on the scale efficiency model, which points to the significance of the pre-processing in data mining. In this article, some methods were utilized; it is noteworthy that Bayesian boosting method led to better results in identifying the factors affecting LOS (accuracy 95.17%). In addition, it was found that 58% of patients younger than 15 years old and 74% of the elderly within the age range of 74–88 were more vulnerable to pneumonia disease. Also, it was found that the Meropenem is a relatively more effective medicine compared to other antibiotics which are used to treat pneumonia in the majority of age groups. Regardless of the impact of various laboratory findings (including CRP, ESR, WBC, NA, K), the patients LOS decreased as a result of Meropenem.



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