Risk adjusted hospital mortality prediction model: a case study in a Turkish training and research hospital

Year 2019, Volume: 48 Issue: 3, 883 - 896, 15.06.2019

Fatma Güntürkün Özgül Vupa Çilengiroğlu

Abstract

In today's world, health organizations give much importance to quality and patient safety. To this end, conservation of life and prevention of excessive deaths are one of the vital objectives for health services in all countries \cite{Ref1}. Although the main function of hospitals is to save lives, there is a little attention to hospital mortality. In this context; generating reliable mortality statistics and then monitoring them is a prerequisite for improvement in care and development in patient safety. In this study; a risk adjusted hospital mortality prediction model is developed by using some popular data mining techniques; logistic regression, decision trees, random forests and artificial neural networks. The data from 30182 inpatients of one of the Turkish training and research hospitals with 1155 beds is used. The data is collected from inpatients whose discharge period is January to November in 2014. At the end, the performance of these approaches are compared.

Keywords

Risk adjusted mortality, Logistic regression, Decision trees, Random forest, Artificial neural networks

References

• Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project (HCUP) - Clinical Classifications Software (CCS), 2013
• Agency for Healthcare Research and Quality. ICD10-CCS. http://www.ahrq.gov/downloads/pub/ccs/icd10ccs.txt. Accessed 2014.
• Allison P. What is the Best R-Squared for Logistic Regression, http://statisticalhorizons.com/r2logistic, Published 2013, Accessed 2015.
• Aylin P, Bottle A, Jen M, Middleton S. HSMR Mortality Indicators. Dr Foster Unit at Imperial College; 2010.
• Berry M, Linoff G. Mastering Data Mining: The Art and Science of Customer Relationship Management (New York: Wiley Computer Pub; 2000).
• Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees (New York: Chapman and Hall; 1984).
• Campbell MJ, Jacques RM, Fotheringham J, Pearson T, Maheswaran R, Nicholl J. Devel- oping A Summary Hospital Mortality Index: Retrospective Analysis in English Hospitals Over Five Years, BMJ 344, 2012.
• Charlson M, Pompei P, Ales K, MacKenzie R. A New Method of Classifying Prognostic Comorbidity in Longitudinal Studies: Development and Validation, Journal of Chronic Dis- eases 40 (5), 373-383, 1987.
• Daniels J, Clarke L, Swain K, Studley R. Mortality Statistics in Wales., Statistical Article Erthygl Ystadegol, 2013.
• Desharnais S, Chesney JD, Wroblewski RT, Fleming ST, Mcmahon LF The Risk-Adjusted Mortality Index: A New Measure of Hospital Performance, Medical Care. 1129-1148, 1988.
• Dr Foster Intelligence. Understanding HSMRs: A Toolkit on Hospital Standardized Mortal- ity Ratios, London, 2012.
• Field A. Discovering Statistics Using SPSS (2nd ed.), (London: Sage, 2005).
• Garcia V, Sanchez JS, Mollineda RA On The Effectiveness of Preprocessing Methods When Dealing With Different Levels of Class Imbalance, Knowledge Based Systems 25, 13-21, 2012.
• Hair JF, Black WC, Babin B, Anderson RE, Tatham RL. Multivariate Data Analysis (6th ed) (Upper Saddle River, NJ: Prentice-Hall; 2006).
• Information Services Division (ISD). ISD. Hospital Standardized Mortality Ratios Quarterly HSMR Release, 2013.
• Jarman B. In Defence of the Hospital Standardized Mortality Ratio, Healthcare Papers, 37-42, 2008.
• Jarman B, Pieter D, van der Veen A, Kool RB, Aylin P, Bottle A, Jones S. The Hospital Standardized Mortality Ratio: A Powerful Tool for Dutch Hospitals to Assess Their Quality of Care, Quality Safety Health Care, 9-13, 2010
• Kass, GV. An Exploratory Technique For Investigating Large Quantities of Categorical Data, 29, 1980.
• NHS. Understanding and Interpreting Mortality Data, http://www.cwmtafuhb.wales.nhs.uk/opendoc/223642, Accessed 2013.
• Satyasree KPNV, Murthy JVR An Exhaustive Literature Review on Class Imbalance Prob- lem, International Journal of Emerging Trends & Technology in Computer Science, 2, 109- 118, 2013.
• Wen E, Sandoval C, Zelmer J, Webster G Understanding and Using the Hospital Standard- ized Mortality Ratio in Canada: Challenges and Opportunities., Healthcare Papers, 26-36, 2008.
• Whalley L. Report from the Steering Group for the National Review of the Hospital Stan- dardized Mortality Ratio, NHS Information Centre for Health and Social Care, 2010.
• Williams L. and Davidge M. 1000 Lives. A Guide to Measuring Mortality., http://www.1000livesplus.wales.nhs.uk Published 2010, Accessed 2014.