Search

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.

PurposeThe rise of the mean age incremented the occurrence of femur fractures with respect to the past, leading thus to serious consequences, as regards morbidity and socio-economic impact. The direction of the A.O.R.N. Cardarelli of Naples has introduced a DTAP whose aim was the reduction of LOS. The paper aims to discuss this issue.Design/methodology/approachThe aim of this paper is to analyze the introduction of DTAP, employing Lean Thinking and Six Sigma methodology based on the DMAIC cycle. To evaluate the effectiveness of DTAP, two groups of patients have been observed for 14 months (before and after the implementation of DTAP).FindingsStatistical tests were performed on the groups and graphics were provided to visualize the decrease of LOS (29.9 per cent). The overall population was also divided in subgroups according to six variables potentially influencing LOS.Research limitations/implicationsAuthors considered six variables of influences; yet, others could be taken into account in the future.Practical implicationsThe decrease of costs due to the management of elderly patients with femur fracture, the optimization of care processes in hospitals and a faster recovery for patients is the tangible contribute of DTAP.Originality/valueThe implementation of DTAP allowed the hospital to obtain a significant reduction of LOS with a consequently decrease of costs alleviating the hospital and the society from the socio-economic burden and the morbidity of this pathology.

Introduction. Maxillary and mandibular cancer represents the sites where cystic and neoplastic conditions, of a benign or malignant nature, can occur. In general, for patients undergoing surgery to remove cancer of the oral cavity, the administration of oral antibiotics, Ceftriaxone and Cefazolin plus Clindamycin, can affect the Length of Stay (LOS).

The development of automated and coordinated driving systems (platooning) is an hot topic today for vehicles and it represents a challenging scenario that heavily relies on distributed control in the presence of wireless communication network. To actuate platooning in a safe way it is necessary to design controllers able to effectively operate on informations exchanged via Inter-Vehicular Communication (IVC) systems despite the presence of unavoidable communication impairments, such as multiple time-varying delays that affect communication links. To this aim in this paper we propose a novel distributed adaptive collaborative control strategy that exploits information coming from connected vehicles to achieve leader synchronization and we analytically demonstrate its stability with a Lyapunov-Krasovskii approach. The effectiveness of the proposed strategy is shown via numerical simulations in P lexe , a state of the art IVC and mobility simulator that includes basic building blocks for platooning.

This paper is concerned with the problem of designing a decentralized consensus protocol for platooning of non-identical vehicles in the presence of heterogeneous time-varying communication delays. The proposed control protocol makes use of a state feedback and to this aim drivetrain dynamics are modeled as third-order linear systems. Necessary and sufficient conditions for convergence and exponential stability, derived by using an appropriate Krasovskii functional, demonstrate the ability of the platoon in reaching the required regime with an exponentially bounded behavior. The proposed LMI-based approach allows to estimate both delay margin and decay rate. Moreover, convergence is proven under switching communication network topologies by means of a Lyapunov-Razumikhin function, and the assessment of a string stable behavior has been also theoretically investigated. High-fidelity simulations with P lexe show the effectiveness of the theoretical results in different driving conditions and in the presence of external disturbances and communication impairment. Different communication channel models are used in the validation stage to further prove robustness of the proposed methodology with respect hard delay and packets losses.

Publisher's version (útgefin grein), Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction., The authors wish to thank Alec Shawn for his contribute as regards "grammar and spell check". This work has been realized thanks to the collaboration of the Department of Advanced Biomedical Sciences of the University Hospital "Federico II" of Naples. The authors wish to thank Sabrina De Vita, Francesca D'Agostino, Giuseppina Toscano and Tania Di Monda for their valuable contribution to the implementation of data mining algorithm during their MS thesis. The work has been partially carried out under TablHealth [CUP B49J17000720008] project and AK12 s.r.l.", "Peer Reviewed"

In recent years the application of Machine Learning (ML) and Artificial Intelligence (AI) techniques in healthcare helped clinicians to improve the management of chronic patients. Diabetes is among the most common chronic illness in the world for which often is still challenging do an early detection and a correct classification of type of diabetes to an individual. In fact it often depends on the circumstances present at the time of diagnosis, and many diabetic individuals do not easily fit into a single class. The aim is this paper is the application of ML techniques in order to classify the occurrence of different mellitus diabetes on the base of clinical data obtained from diabetic patients during the daily hospitals activities.

We theoretically address the problem of achieving third-order consensus for a platoon of vehicles in the presence of heterogenous time-varying communication delays. A Lyapunov-Krasovkii function is constructed for the stability analysis. Necessary and sufficient conditions are derived to guarantee that all vehicles asymptotically reach the relative acceleration, speed and position according to the spacing policy. Numerical simulations are provided to demonstrate the effectiveness of the theoretical results in different driving conditions and in the presence of external disturbances. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The domain of medical decision making process is heavily affected by vagueness and uncertainty issues and — for copying with them — different type of Clinical Decision Support System (CDSS)s, simulating human expert clinician reasoning, have been designed in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the knowledge-based CDSS actually used in the day-by-day clinical care of β-thalassemia patients of the Rare Red Blood Cell Disease Unit (RRBCDU) at Cardarelli Hospital (Naples, Italy). All the designed functionalities were iteratively developed on the field, through requirement-adjustment/development/validation cycles executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The paper shows exemplary results on the on-line evaluation of Iron Overload during the health status assessment and care management of β-Thalassemia patients.

Automated and coordinated vehicles' driving (platooning) is very challenging due to the multibody control complexity and the presence of unreliable time-varying wireless intervehicular communication (IVC). We propose a novel controller for vehicle platooning based on consensus and analytically demonstrate its stability and dynamic properties. Traditional approaches assume the logical control topology as a constraint fixed a priori , and the control law is designed consequently; our approach makes the control topology a design parameter that can be exploited to reconfigure the controller, depending on the needs and characteristics of the scenario. Furthermore, the controller automatically compensates outdated information caused by network losses and delays. The controller is implemented in Plexe , which is a state-of-the-art IVC and mobility simulator that includes basic building blocks for platooning. Analysis and simulations show the controller robustness and performance in several scenarios, including realistic propagation conditions with interference caused by other vehicles. We compare our approach against a controller taken from the literature, which is generally considered among the most performing ones. Finally, we test the proposed controller by implementing the real dynamics (engine, transmission, braking systems, etc.) of heterogeneous vehicles in Plexe and verifying that platoons remain stable and safe, regardless of real-life impairments that cannot be modeled in the analytic solution. The results show the ability of the proposed approach to maintain a stable string of realistic vehicles with different control-communication topologies, even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.