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.