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M. A. Denai, M. Mahfouf and J. J. Ross, “A Hybrid Hierarchical Decision Support System for Cardiac Surgical Intensive Care Patients, Part I—Physiological Modelling and Decision Support Design,” Artificial Intelligence in Medicine, Vol. 45, No. 1, 2009, pp. 35-52. http://dx.doi.org/10.1016/j.artmed.2008.11.009

has been cited by the following article:

  • TITLE: Recursive Fuzzy Predictions of Future Patient Paths to Support Clinical Decision Making in ICU

    AUTHORS: A. Zeghbib, M. Mahfouf, J. J. Ross, G. H. Mills, G. G. Panoutsos, M. Denai, S. Suzani

    KEYWORDS: Proactive Treatment, Clinical Decision, Intensive-Care, Patient-Paths, Physiological Map

    JOURNAL NAME: Engineering, Vol.5 No.10B, October 17, 2014

    ABSTRACT: In this paper, we propose a new architecture that combines prediction and decision-making in the form of a hybrid framework aimed at providing clinicians with transparent and accurate maps, or charts, to guide and to support treatment decisions, and to interrogate the clinical patients’ course as it develops. These maps should be patient-specific, with options displayed of possible treatment pathways. They would suggest the optimal care pathways, and the shortest routes to the most efficient care, by predicting clinical progress, testing the ensuing suggestions against the developing clinical state and patient condition, and suggesting new options as necessary. These maps should also mine an extensive database of accumulated patient data, modelled diseases, and modelled patient-responses based on expert-derived rules. These individualized hierarchical targets, which are implemented in order to prevent life-threatening illnesses, will also have to “adapt” to the patient’s altering clinical condition. Therapies that support one system can destabilize others and selecting which specific support to prioritize is an uncertain process, the prioritization of which can vary between clinical experts. Whilst clinical therapeutic decisions can be made with some degree of anticipation of the “likely” outcome (based on the experts’ opinion and judgment), treatment is essentially rooted in the present, and is dependent on analyzing the current clinical condition and available data. The recursive learning approach presented in this paper, allows decision rules to predict the possible future course, and reflects back derived information from such projections to the present time and thus support proactive clinical care rather than reactive clinical care. The proposed framework for such a patient map supports and enables an optimized choice from available options and also ensures that decisions are based on both the available evidence and a database of best clinical practice. Preliminary results are encouraging and it is hoped to validate the approach clinically in the near future.