A Clinical Support System Based on Quality of Life Estimation

Abstract

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.

A Review of Commercial and Medical-Grade Physiological Monitoring Devices for Biofeedback-Assisted Quality of Life Improvement Studies

“…Most studies on quality of life have been focused on chronical diseases or others that somehow have a high impact on the patient's lifestyle, and sometimes requires the patient to make changes to life habits. In cancer in particular, QOL has been studies for more than three decades, with research works focused on particular cancer types and the impact on the individual quality of life [27][28][29][30][31], as well as more general research works by Aaronson [32] and Rehse [33].…”

Development and Validation of a Health and Work Survey Based on the Rasch Model among Portuguese Workers

“…The selection of this psychometric validation model is supported by the following factors: it is a model with a strong tradition in the field of health [39,40] and was originally developed for binary data (most INSAT variables are precisely dichotomous or ordinal type); the validation model is Bsample- [41,50,51]; it has a probabilistic and not deterministic character and, therefore, is consistent with the type of statistical analysis commonly-used in the treatment of INSAT results (nonparametric and logistic regression tests) [29][30][31]; and the Rasch model allows for invariance test items, which is of particular importance considering the cross-cultural use of instruments of this type. This model specifies that each item has its own rating scale structure.…”

Fatores associados à qualidade de vida relacionada à saúde de adolescentes

“…(2) No contexto das Ciências da Saúde, surge um conceito mais específico, a Qualidade de Vida Relacionada à Saúde (QVRS) ou saúde percebida que é a autoavaliação que o indivíduo faz sobre o seu estado de saúde atual em comparação às suas expectativas pessoais e sob a influência de fatores externos, como a duração ou severidade da doença e o nível do suporte social ou familiar recebido. (3) A investigação da QV no âmbito das pesquisas em saúde encontra-se em crescente expansão e, apesar da relevância, é pouco estudada na população adolescente. A exploração da temática ganhou maior destaque a partir da última década e o Brasil é o segundo país que mais investiga o assunto, ficando atrás apenas dos Estados Unidos da América.…”