Using probabilistic graphical models to enhance the prognosis of health-related quality of life in adult survivors of critical illness

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Claudia Camila Dias (Author) Other; Cristina Granja (Author) Other; Altamiro Costa Pereira (Author) FMUP; Joao Gama (Author) FEP; Pedro Pereira Rodrigues (Author) FMUP

Conference proceedings International

Title: 2014 IEEE 27TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Pages: 56-61

27th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

New York, NY, MAY 27-29, 2014

Indexing

ISI Web of Knowledge - 3 Citations

ISI Web of Science

Scopus - 3 Citations

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

CORDIS: Health sciences

Other information

Authenticus ID: P-009-TRX

DOI: 10.1109/cbms.2014.31

Abstract (EN): Health-related quality of life (HR-QoL) is a subjective concept, reflecting the overall mental and physical state of the patient, and their own sense of well-being. Estimating current and future QoL has become a major outcome in the evaluation of critically ill patients. The aim of this study is to enhance the inference process of 6 weeks and 6 months prognosis of QoL after intensive care unit (ICU) stay, using the EQ-5D questionnaire. The main outcomes of the study were the EQ-5D five main dimensions: mobility, self-care, usual activities, pain and anxiety/depression. For each outcome, three Bayesian classifiers were built and validated with 10-fold cross-validation. Sixty and 473 patients (6 weeks and 6 months, respectively) were included. Overall, 6 months QoL is higher than 6 weeks, with the probability of absence of problems ranging from 31% (6 weeks mobility) to 72% (6 months self-care). Bayesian models achieved prognosis accuracies of 56% (6 months, anxiety/depression) up to 80% (6 weeks, mobility). The prognosis inference process for an individual patient was enhanced with the visual analysis of the models, showing that women, elderly, or people with longer ICU stay have higher risk of QoL problems at 6 weeks. Likewise, for the 6 months prognosis, a higher APACHE II severity score also leads to a higher risk of problems, except for anxiety/depression where the youngest and active have increased risk. Bayesian networks are competitive with less descriptive strategies, improve the inference process by incorporating domain knowledge and present a more interpretable model. The relationships among different factors extracted by the Bayesian models are in accordance with those collected by previous state-of-the-art literature, hence showing their usability as inference model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6