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

MOTIVATION Pneumonia 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. METHODS The 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 CONCLUSIONS The 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.

[1]  M. G. Castillo Modelling patient length of stay in public hospitals in Mexico , 2012 .

[2]  Steven Walczak,et al.  Predicting Hospital Length of Stay with Neural Networks , 1998, FLAIRS.

[3]  Jerrold H. May,et al.  Identification of readmission risk factors by analyzing the hospital-related state transitions of congestive heart failure (CHF) patients , 2015 .

[4]  Jerrold H. May,et al.  A mixed-ensemble model for hospital readmission , 2016, Artif. Intell. Medicine.

[5]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[6]  David W Shofler,et al.  The impact of comorbidities on inpatient Charcot neuroarthropathy cost and utilization. , 2016, Journal of diabetes and its complications.

[7]  Pavel Brazdil,et al.  Factors influencing hospital high length of stay outliers , 2012, BMC Health Services Research.

[8]  E. El-Darzi,et al.  Healthcare Data Mining: Prediction Inpatient Length of Stay , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

[9]  Joseph Futoma,et al.  A comparison of models for predicting early hospital readmissions , 2015, J. Biomed. Informatics.

[10]  M. Cakici,et al.  A Retrospective Analysis of Surgical Femoral Artery Closure Techniques: Conventional versus Purse Suture Technique. , 2017, Annals of vascular surgery.

[11]  Dinesh U Acharya,et al.  Comparison of Different Data Mining Techniques to Predict Hospital Length of Stay , 2011 .

[12]  Karim Keshavjee,et al.  Performance Analysis of Data Mining Classification Techniques to Predict Diabetes , 2016 .

[13]  Hong-Dar Isaac Wu,et al.  Model-based prediction of length of stay for rehabilitating stroke patients. , 2009, Journal of the Formosan Medical Association = Taiwan yi zhi.

[14]  Vandana Pursnani Janeja,et al.  Predicting Hospital Length of Stay (PHLOS): A Multi-tiered Data Mining Approach , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[15]  P. Harper,et al.  A review and comparison of classification algorithms for medical decision making. , 2005, Health policy.

[16]  K. Cheever,et al.  Brunner and Suddarth's Textbook of Medical-Surgical Nursing , 1992 .

[17]  Wei Zhong,et al.  Clinical charge profiles prediction for patients diagnosed with chronic diseases using Multi-level Support Vector Machine , 2012, Expert Syst. Appl..

[18]  M. Blais,et al.  Predicting Length of Stay on an Acute Care Medical Psychiatric Inpatient Service , 2003, Administration and Policy in Mental Health and Mental Health Services Research.

[19]  T Shohat,et al.  Factors associated with inappropriate hospitalization days in internal medicine wards in Israel: a cross-national survey. , 1998, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[20]  Leonid Churilov,et al.  Knowledge Discovery through Mining Emergency Department Data , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[21]  F. Abelha,et al.  Determinants of Outcome in Patients Admitted to a Surgical Intensive Care Unit , 2007 .

[22]  K. Swedberg,et al.  Factors influencing the length of hospital stay of patients with heart failure , 2003, European journal of heart failure.

[23]  Clare Bates Congdon,et al.  Estimating Patient's Length of Stay in the Emergency Department with an Artificial Neural Network , 2005, AMIA.

[24]  Jerrold H. May,et al.  Insights from a machine learning model for predicting the hospital Length of Stay (LOS) at the time of admission , 2017, Expert Syst. Appl..

[25]  Sang Won Yoon,et al.  Predictive modeling of hospital readmissions using metaheuristics and data mining , 2015, Expert Syst. Appl..

[26]  Filip De Turck,et al.  Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores , 2015, Artif. Intell. Medicine.

[27]  Arab Mohammad,et al.  Analysis of Factors Affecting Length of stay in Public Hospitals in Lorestan Province, Iran , 2010 .

[28]  Timothy Y. Wang,et al.  Impact of Intraoperative Steroids on Postoperative Infection Rates and Length of Hospital Stay: A Study of 1200 Spine Surgery Patients. , 2016, World neurosurgery.

[29]  Paulo Cortez,et al.  Using Data Mining for Prediction of Hospital Length of Stay: An Application of the CRISP-DM Methodology , 2014, ICEIS.

[30]  Simon M. Hsiang,et al.  Incorporating the dynamics of epidemics in simulation models of healthcare systems , 2014, Simul. Model. Pract. Theory.

[31]  Neil O'Hare,et al.  The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors , 2007, Artif. Intell. Medicine.

[32]  Ivan Bratko,et al.  Machine Learning and Data Mining; Methods and Applications , 1998 .

[33]  Lauren B. Davis,et al.  Using Data Mining to Analyze Patient Discharge Data for an Urban Hospital , 2010, DMIN.

[34]  G. Pichler,et al.  Maternal stress after preterm birth: Impact of length of antepartum hospital stay. , 2016, Women and birth : journal of the Australian College of Midwives.

[35]  J V Tu,et al.  Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery. , 1992, Proceedings. Symposium on Computer Applications in Medical Care.

[36]  Peyman Rezaei Hachesu,et al.  Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients , 2013, Healthcare informatics research.