iCare4U – Decision support system for personalized medicine in ICUs
This project proposes a decision support system based on intelligent modelling and patient sub-group analysis, to provide personalized therapy for critically ill patients. It is hypothesized that the “Collective Experience” from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the ICU. The validity of the proposed systems will be tested using two case studies where generalized severity scoring systems have consistently performed poorly: patients admitted to the ICU who then develop acute kidney injury, where two-thirds of these patients require renal support therapy; and severe sepsis a typical heterogeneous disease among critically ill patients, in which the immune response is highly dynamic and variable, and likely to need complex therapies to improve patient outcomes.
This project will use data from the MIMIC III database, that gather information of ICU patients admitted to the Beth Israel Deaconess Medical Center from 2001 to 2014. It will also use data from a Philips Healthcare database that gathers data from 333 different hospitals, 1,799,500 patients from 835 ICUs. Further, it will also use a database collected from a Portuguese Hospital, Hospital Beatriz Angelo, which will allow the comparison of cross-country results in two different environments.
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The five most representative publications from the team in this area.
[a] Vieira, S.M., Mendonça, L.F., Farinha, G.J. and Sousa, J.M.C.. Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Applied Soft Computing, Volume 13, Issue 8, pp. 3494–3504, August 2013.
[b] J.M.C. Sousa and U. Kaymak. Fuzzy Decision Making in Modeling and Control. World Scientific Series in Robotics & Intelligent Systems. World Scientific Pub. Co., Singapore, December 2002.
[c] F. Cismondi, L.A. Celi, A.S. Fialho, S.M. Vieira, S.R. Reti, J.M.C. Sousa and S. N. Finkelstein. Reducing unnecessary lab testing in the ICU with artificial intelligence. International Journal of Medical Informatics, 82(5):345-358, May 2013.
[d] R Henriques, SC Madeira, BicPAM: Pattern-based biclustering for biomedical data analysis. Algorithms for Molecular Biology 9 (1), 27, 2014.
[e] Nuno Homem and Joao P. Carvalho, Finding top-k elements in data streams, Information Sciences, 180(24), pp. 4958-4974, Dec. 2010, Elsevier.