A Clinical Support System Based on Quality of Life Estimation
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Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual’s daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt’s sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.
KeywordsQuality of life Cancer Information technologies Clinical support system Data mining
This work was funded by QoLis - Quality of Life Platform Project, N°2013/34034 QREN SI I&DT, (NUP, NORTE-07-0202-FEDER-034Ú34). The authors also acknowledge: LIACC (PEst-OE/EEI/UI0027/2014).
- 1.Marchibroda, J. M., The impact of health information technology on collaborative chronic care management. J. Manag. Care Pharm. 14(2 Suppl):3–11, 2008.Google Scholar
- 2.Tenório, J., Hummel, A., Sdepanian, V., Pisa, I., and Marin, H. F., Experiências internacionais da aplicação de sistemas de apoio à decisão clínica em gastroenterologia. J Health Inf 3(1):27–31, 2011.Google Scholar
- 3.Georga, E., Protopappas, V., Guillen, A., Fico, G., Ardigo, D., Arredondo, M. T., Exarchos T. P., Polyzos, D., Fotiadis, D. I., Data mining for blood glucose prediction and knowledge discovery in diabetic patients: The METABO diabetes modeling and management system, Eng. in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE, pp. 5633-5636, 2009.Google Scholar
- 5.Berner, E. S., Clinical decision support system: State of the Art. AHRQ Publication, n° 09.0069 – EF. Agency for Healhcare Research and Quality, Rockville, 2009.Google Scholar
- 14.Deshpande, K., and Ganz, A., DiNAR: Health monitoring of IT systems in emergency response. Conf Proc IEEE Eng Med Biol Soc 1:1699–1702, 2009.Google Scholar
- 16.Pimentel, F., Qualidade de Vida do Doente Oncológico. De autor, 2003.Google Scholar
- 20.Gonçalves, J., and Rocha, Á., Decision support system for quality of life in head and neck oncology patients. Head Neck Oncol. 4(3):1–9, 2012.Google Scholar
- 21.Randall E, Schumacker P (2005) Item response theory. Applied Measurement Associates.Google Scholar
- 22.Castro, S., Teoria de Resposta ao Item: Aplicação na avaliação de sintomas depressivos. PhD Thesis Univ. Fed. Rio Grande do Sul, 2008.Google Scholar
- 23.Mead, R., The Measurement Theory of Georg Rasch. Data Recognition Corporation, 2008.Google Scholar
- 24.Rapidminer, Available at: http://rapidminer.com/, Consulted in: April 2015.
- 25.Zhang, H., The Optimality of Naive Bayes. Faculty of Computer Science, University of New Brunswick, Frederic-ton, New Brunswick, Canada, American Association for Artificial Intelligence, 2004.Google Scholar
- 26.Platt, J., Machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., and Smola, A. (Eds.), Advances in Kernel Methods - Support Vector Learning, 1998.Google Scholar