The Paper Feed

The Ecosystem Management Decision Support (EMDS) system has been further enhanced with an analytical engine—BayesFusion’s SMILE (Structural Modeling, Inference, and Learning Engine) that comes with the GeNIe (Graphical Network Interface) software—for creating Bayesian network (BN) models. BNs are graphical networks of variables linked by probabilities, that have proven useful in decision-aiding for risk analysis and risk management.

Glycemic control in intensive care patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide personalized automated recommendations. One of the most promising solutions for this is the STAR protocol, which is based on a clinically validated insulin-nutrition-glucose physiological model. However, this approach does not consider demographical background such as age, weight, height, and ethnicity. This article presents the extension to intensive care personalized solution by integrating per-patient demographical, and upon admission information to intensive care conditions to automate decision support for clinical staff. In this context, a virtual study was conducted on 210 retrospectives intensive care patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and performance of the STAR control. The proof of concept shows 71.43% and 73.90% overall inference precision, and reliability, respectively, on the test dataset. With more data, improved Bayesian Network is believed to be reproduced. These results, nevertheless, points at the feasibility of the network to act as an effective classifier using intensive care units data, and glycemic control performance to be the basis of a probabilistic, personalized, and automated decision support in the intensive care units.

The “Leaky noisy-OR” is a common method used to simplify the elicitation of complex conditional probability tables in Bayesian networks involving Boolean variables. It has proven useful for approximating the required relationship in many real-world situations where there are two or more variables that are potential causes of a single effect variable. However, one of the properties of leaky noisy-OR is Conditional Inter-causal Independence (CII). This property means that ‘explaining away‘ behaviour-one of the most powerful benefits of BN inference is not present when the effect variable is observed as false. Yet, for many real-world problems where the leaky noisy-OR has been considered, this behaviour would be expected, meaning that leaky noisy-OR is deficient as an approximation of the required relationship in such cases. There have been previous attempts to adapt noisy-OR to resolve this problem. However, they require too many additional parameters to be elicited. We describe a simple but powerful extension to leaky noisy-OR that requires only a single additional parameter. While it does not solve the CII problem in all cases, it resolves most of the explaining away deficiencies that occur in practice. The problem and solution is illustrated using an example from intelligence analysis.