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Purpose: Explore how efficient intelligent decision support systems, both easily understandable and straightforwardly implemented, can help modern hospital managers to optimize both bed occupancy and utilization costs.Methods and Materials: This paper proposes a hybrid genetic algorithm-queuing multi-compartment model for the patient flow in hospitals. A finite capacity queuing model with phase-type service distribution is combined with a compartmental model, and an associated cost model is set up. An evolutionary-based approach is used for enhancing the ability to optimize both bed management and associated costs. In addition, a "What-if analysis" shows how changing the model parameters could improve performance while controlling costs. The study uses bed-occupancy data collected at the Department of Geriatric Medicine - St. George's Hospital, London, period 1969-1984, and January 2000.Results: The hybrid model revealed that a bed-occupancy exceeding 91%, implying a patient rejection rate around 1.1%, can be carried out with 159 beds plus 8 unstaffed beds. The same holding and penalty costs, but significantly different bed allocations (156 vs. 184 staffed beds, and 8 vs. 9 unstaffed beds, respectively) will result in significantly different costs (£755 vs. £1172). Moreover, once the arrival rate exceeds 7 patient/day, the costs associated to the finite capacity system become significantly smaller than those associated to an Erlang B queuing model (£134 vs. £947).Conclusion: Encoding the whole information provided by both the queuing system and the cost model through chromosomes, the genetic algorithm represents an efficient tool in optimizing the bed allocation and associated costs. The methodology can be extended to different medical departments with minor modifications in structure and parameterization. [ABSTRACT FROM AUTHOR]

Objective. To investigate the potential of combined sonographic and clinical determinations to predict the mode of delivery at term. Materials and method. This observational prospective cohort study was deployed in a tertiary maternity hospital (University County Emergency Hospital Craiova). Unselected low-risk primiparous pregnant women were weekly evaluated at term for ultrasound determinations (estimated fetal weight [EFW]), head descent parameters, occiput position [OP], cervical length), Bishop score and maternal characteristics (age, height, weight). A thorough statistical analysis determined which variables were significantly correlated with the delivery mode. Results. Data from 276 term primiparous were analyzed. Head descent parameters were strongly and significantly correlated with each other, but only progression distance (PD) was correlated with the delivery mode (weeks 37, 38, 41, and the week before delivery). The week before delivery (WBD) measurements of head to perineum distance (HPD) and angle of progression (AOP) reached a close to significance p level: 0.055 and 0.07, respectively. The following variables were significantly correlated with the delivery mode: BMI in all term evaluations, along with PD measured at 37 and 38 weeks, maternal age for week 39, Bishop, EFW and OP for week 40 and BMI, EFW, and PD evaluated during the WBD. We have also provided logistic regression equations for each week that correctly forecasted the delivery mode, except for week 38. Cut-off values have been established for each significant parameter per week. The cut-off values must be read in conjunction with the AUC, which ranges from 0.55 to 0.73, depending on the variable. Conclusions. There are strong and significant correlations between the “head descent” ultrasound measurements at term. BMI is predictive regarding labor outcome throughout term evaluations. PD and BMI measured at 37-38 weeks correlate with the delivery mode and apparently can be used to forecast the delivery mode when the pregnancy reaches term. WBD measurements of EFW and PD can be used to forecast the delivery mode, perhaps within a policy addressed to pregnant women with pre-labor clinical signs. Larger studies are needed, due to the need for more and better-balanced data. [ABSTRACT FROM AUTHOR]

Genetic algorithms ( GAs) and neural networks ( NNs) are both inspired by computation in biological systems and many attempts have been made to combine the two methodologies to boost the NNs performance. This paper deals with the evolutionary training of a feedforward NN for both breast cancer detection and recurrence. A multi-layer perceptron ( MLP) has been designed for this purpose, using a GA routine to set weights, and a Java implementation of this hybrid model has been made. Four databases concerning cancer detection and recurrence have been used, two databases containing numerical attributes only, one database containing ordinal (categorical) attributes solely and one database with mixed attributes. In comparison to some standard NNs, the performance of this approach using the same databases is shown to be superior. Moreover, this hybrid MLP/ GA model is very flexible in terms of providing accurate classification, even with different types of attributes, which is usually found in medical studies. [ABSTRACT FROM AUTHOR]

Abstract: Hepatic fibrosis represents the principal pointer to the development of liver diseases. The correct evaluation of its degree, based on both recent non-invasive procedures and machine learning models, is of current major concern. One of the latest medical imaging methodologies for assessing it is the Fibroscan, supported by biochemical and clinical examinations. Since the complex interaction between the Fibroscan stiffness indicator and the biochemical and clinical results is hard to be manually managed towards the liver fibrosis stadialization, well-performing machine learning algorithms have been proposed to support an automatic diagnosis. We propose in this paper a tandem feature selection mechanism and evolutionary-driven neural network as a computer-based support for liver fibrosis stadialization in chronic hepatitis C. A synergetic system, based on both specific statistical tools and the sensitivity analysis provided by neural networks is used for reducing the dimension of the database from twenty-five to just six attributes. An evolutionary-trained neural network is developed afterwards for the classification of the liver fibrosis stages. The tandem approach is direct and simple, resulting from embedding the feature selection system into the method structure, in order to dynamically concentrate the search only on the most relevant attributes. Experimental results and a thorough statistical analysis clearly demonstrated the efficiency of the proposed intelligent system in comparison with other machine learning techniques reported in literature. [Copyright &y& Elsevier]

• Two efficient extensions of the LogSLFN for multiple decision classes are proposed. • Experimental results on 2 datasets to compare the proposed models are presented. • A statistical comparison between the parallel and cascaded extensions is done. • The classification accuracy for the parallel extension of the LogSLFN is consistent and high. • A statistical analysis of the proposed models versus state-of-the-art ones is performed. An important step towards a better healthcare system is fast and accurate diagnosis. In the last decade, the application of intelligent systems in healthcare has led to impressive results. The goal of this paper is to extend the LogSLFN (single-hidden layer feedforward neural network trained using logistic regression) algorithm, which has been deployed successfully in the past for the case of a two-class decision problem, to the case of multiple classes. We have considered and statistically analyzed two approaches: a parallel LogSLFN, and a cascaded LogSLFN. According to the universal approximation theorem, a single-hidden layer feedforward neural network has the ability to approximate arbitrarily closely continuous functions of several real variables under certain reasonable assumptions. Essentially, a single hidden layer containing a finite fixed number of neurons is sufficient to provide an arbitrarily well approximation to a given training set of inputs and a desired target output represented by a continuous function. Parallel LogSLFN and cascaded LogSLFN are two novel approaches that can be applied to multiple-class decision problems. Both methods are extensions of the LogSLFN, which uses logistic regression to compute the weights between the input and hidden layer of a single-hidden layer feedforward network. No error correction is needed, the weights between the hidden and the output layer being computed using the Moore-Penrose pseudoinverse matrix. The proposed models have been tested on two medical datasets regarding cancer diagnosis and liver fibrosis staging. Experimental results and the subsequent statistical analysis have proved the robustness of the proposed models with other machine learning techniques reported in literature. The experimental results showed that the Parallel approach surpasses the Cascaded one. Still, both models are competitive to the other state-of-the-art techniques. The LogSFLN algorithm can be successfully extended to multiple-class decision problems. By embedding knowledge extracted from the data into the architecture, we obtained a raise by 20% in accuracy when applied on the liver fibrosis dataset. [ABSTRACT FROM AUTHOR]

The use of computer technology to support medical decisions is now widespread and pervasive across a broad range of medical areas. Accordingly, computer-aided diagnosis has become an increasingly important area for intelligent computational systems. This paper describes a competitive/collaborative neural computing system designed to support the medical decision process using medical imaging databases. A concrete example concerning an application to support the differential diagnosis of chronic pancreatitis and pancreatic cancer is also provided. [ABSTRACT FROM AUTHOR]