An innovative system to assist the mobility of people with motor disabilities

International audiencePeople with motor disabilities require assistance for navigating form one location to another. In order to improve the integration of wheelchair users into their daily life and work, we propose a real time adaptive planning algorithm for routing the user through an obstacle free optimal path. Our application is based on an augmented reality system for the assistance of wheelchair people (ARSAWP) and uses augmented reality (AR) smart glasses. The main goal is to support the development of indoor and outdoor navigation systems devoted to wheelchair users. In this paper we detail the design, the implementation and the evaluation of the proposed application, which was implemented in java for the Android operational system. Two types of database are used (local database and remote database). The information about navigation is displayed on AR glasses which give the user the possibility to interact with the system according to the external environment. The prototype is designed for use within the University of Lille campus

Physiological Intracranial Calcifications in Children: A computed tomography-based study

Objectives: Physiological intracranial calcifications (PICs) are benign in nature and related to aging. We aimed to study the frequency of physiological intracranial calcifications (PICs) in pediatric population using computed tomography (CT). Methods: The brain CT scans of consecutive patients (age range, 0-15 years) who had visited Sultan Qaboos University Hospital from January 2017 to December 2020 were retrospectively assessed for the presence of PICs. The presence of calcifications was identified using 3 mm thick axial images, and coronal and sagittal reformats. Results: A total of 460 patients were examined and the mean age was 6.54 ± 4.94 years. The frequency of PIC in boys and girls was 35.1% and 35.4%, respectively. PICs were most common in choroid plexus with 35.21% (age range 0.4 -15 years; median, 12 years), followed by the pineal gland in 21.08% (age range 0.5 -15 years; median, 12 years) and the habenular nucleus in 13.04% of subjects (2.9 -15 years; median, 12 years). PICs were less common in falx cerebri with 5.86% (age range 2.8-15 years; median, 13 years) and tentorium cerebelli in 3.04% (age range 7-15 years; median, 14 years) of subjects. PICs increased significantly with increasing age (p<0.001). Conclusion: Choroid plexus is the most frequent site of calcification. Choroid plexus and pineal gland calcifications may be present at less than 1 year of age. Recognizing PICs is clinically important for radiologists as they can be mistaken for hemorrhage or pathological entities like neoplasms or metabolic diseases. Keywords: Calcification; Pineal gland; Dura Mater; Brain; Computed Tomograph

Collaborative optimization by self-adaptive agents to solve patient scheduling problems in inter-intra hospital emergencies

Cette thèse s’attaque à des problèmes d’ordonnancement des patients aux urgences, avec prise en compte des contraintes d’aval, en utilisant des approches d’optimisation collaboratives optimisant le temps d’attente global moyen des patients. Ces approches sont utilisées en intégrant, dans le comportement de chaque agent,une métaheuristique qui évolue efficacement, grâce à deux protocoles d’interaction "amis" et "ennemis". En outre, chaque agent s’auto-adapte à l’aide d’un algorithme d’apprentissage par renforcement adapté a unproblème étudié. Cette auto-adaptation tient compte d’expériences des agents et de leurs connaissances de l’environnement des urgences. Afin d’assurer la continuité d’une prise en charge de qualité des patients,nous proposons également dans cette thèse, une approche conjointe d’ordonnancement et d’affectation des lits d’aval aux patients. Nous illustrons les approches collaboratives proposées et démontrons leur sefficacités sur des données réelles provenant des services des urgences du CHU de Lille obtenues dans le cadre du projet ANR OIILH. Les résultats de simulations donnent des meilleurs ordonnancements par rapport aux scénarios dans lesquels les agents travaillent individuellement ou sans apprentissage.L’application des algorithmes qui gèrent la prise en charge des patients dans les services d’aval, fournit des résultats sous la forme d’un tableau de bord, contenant des informations statiques et dynamiques. Ces informations sont mises à jour en temps réel et permettent aux urgentistes d’orienter plus rapidement les patients vers les structures qui peuvent les accueillir. Ainsi, les résultats des expérimentations montrent que les algorithmes d’IA proposés peuvent améliorer de manière significative l’efficacité de la chaîne des urgences en réduisant le temps d’attente global moyen des patients en inter-intra-urgencesThis thesis addresses the scheduling patients in emergency department (ED) considering downstreamconstraints, by using collaborative optimization approaches to optimize the total waiting time of patients.These approaches are used by integrating, in the behavior of each agent, a metaheuristic that evolvesefficiently, thanks to two interaction protocols "friends" and "enemies". In addition, each agent self-adaptsusing a reinforcement learning algorithm adapted to the studied problem. This self-adaptation considersthe agents’ experiences and their knowledge of the ED environment. The learning of the agents allowsto accelerate the convergence by guiding the search for good solutions towards more promising areas inthe search space. In order to ensure the continuity of quality patient care, we also propose in this thesis,a joint approach for scheduling and assigning downstream beds to patients. We illustrate the proposedcollaborative approaches and demonstrate their effectiveness on real data provided from the ED of the LilleUniversity Hospital Center obtained in the framework of the ANR OIILH project. The results obtainedshow that the collaborative Learning approach leads to better results compared to the scenario in whichagents work individually or without learning. The application of the algorithms that manage the patientscare in downstream services, provides results in the form of a dashboard, containing static and dynamicinformation. This information is updated in real time and allows emergency staff to assign patients morequickly to the adequate structures. The results of the simulation show that the proposed AI algorithms cansignificantly improve the efficiency of the emergency chain by reducing the total waiting time of patientsin inter-intra-emergency

Optimisation collaborative par des agents auto-adaptatifs pour résoudre les problèmes d'ordonnancement des patients en inter-intra urgences hospitalières

This thesis addresses the scheduling patients in emergency department (ED) considering downstreamconstraints, by using collaborative optimization approaches to optimize the total waiting time of patients.These approaches are used by integrating, in the behavior of each agent, a metaheuristic that evolvesefficiently, thanks to two interaction protocols "friends" and "enemies". In addition, each agent self-adaptsusing a reinforcement learning algorithm adapted to the studied problem. This self-adaptation considersthe agents’ experiences and their knowledge of the ED environment. The learning of the agents allowsto accelerate the convergence by guiding the search for good solutions towards more promising areas inthe search space. In order to ensure the continuity of quality patient care, we also propose in this thesis,a joint approach for scheduling and assigning downstream beds to patients. We illustrate the proposedcollaborative approaches and demonstrate their effectiveness on real data provided from the ED of the LilleUniversity Hospital Center obtained in the framework of the ANR OIILH project. The results obtainedshow that the collaborative Learning approach leads to better results compared to the scenario in whichagents work individually or without learning. The application of the algorithms that manage the patientscare in downstream services, provides results in the form of a dashboard, containing static and dynamicinformation. This information is updated in real time and allows emergency staff to assign patients morequickly to the adequate structures. The results of the simulation show that the proposed AI algorithms cansignificantly improve the efficiency of the emergency chain by reducing the total waiting time of patientsin inter-intra-emergency.Cette thèse s’attaque à des problèmes d’ordonnancement des patients aux urgences, avec prise en compte des contraintes d’aval, en utilisant des approches d’optimisation collaboratives optimisant le temps d’attente global moyen des patients. Ces approches sont utilisées en intégrant, dans le comportement de chaque agent,une métaheuristique qui évolue efficacement, grâce à deux protocoles d’interaction "amis" et "ennemis". En outre, chaque agent s’auto-adapte à l’aide d’un algorithme d’apprentissage par renforcement adapté a unproblème étudié. Cette auto-adaptation tient compte d’expériences des agents et de leurs connaissances de l’environnement des urgences. Afin d’assurer la continuité d’une prise en charge de qualité des patients,nous proposons également dans cette thèse, une approche conjointe d’ordonnancement et d’affectation des lits d’aval aux patients. Nous illustrons les approches collaboratives proposées et démontrons leur sefficacités sur des données réelles provenant des services des urgences du CHU de Lille obtenues dans le cadre du projet ANR OIILH. Les résultats de simulations donnent des meilleurs ordonnancements par rapport aux scénarios dans lesquels les agents travaillent individuellement ou sans apprentissage.L’application des algorithmes qui gèrent la prise en charge des patients dans les services d’aval, fournit des résultats sous la forme d’un tableau de bord, contenant des informations statiques et dynamiques. Ces informations sont mises à jour en temps réel et permettent aux urgentistes d’orienter plus rapidement les patients vers les structures qui peuvent les accueillir. Ainsi, les résultats des expérimentations montrent que les algorithmes d’IA proposés peuvent améliorer de manière significative l’efficacité de la chaîne des urgences en réduisant le temps d’attente global moyen des patients en inter-intra-urgence

Analysis and modeling of the key performance indicators in the emergency department

International audienceImproving emergency department (ED) processes requires consideration of multiple variables and objectives. EDs are known as a highly dynamic and unpredictable environment, which makes decision making extremely difficult. The use of different methodologies and tools to support the decision-making process is therefore essential. Nevertheless, medical staff are not adequately trained or prepared to solve such problems. They are completely destitute in methodologies and tools for decision support and management adapted to their future activities. The management of emergency systems generally involves three issues: their conception, planning and control. Conception is the definition/prediction of the future characteristics of the hospital system. Planning involves identifying the various resources used to perform care operations and defining how and when these operations should be performed. Finally, the control aims at correct the inherent disturbances (deterioration in the patient's state of health, new patients to treat). These three key steps are performed according to the frequency of problems encountered during the execution of the scheduling. In this paper, we focus on the first point, which is a presentation of a prediction model and a modeling of the patient pathway in the adult ED (AED) of the Lille University Hospital Center (LUHC). First, we present a statistical real data analysis. We use a real database of the AED provided by the LUHC, which is our partner. This Data collected over a period of four years, from June 2016 until June 2020, thanks to ResUrgence, a software implemented in LUHC. This analysis allows us to determine the most interesting aspects of the functioning of the AED

La végétation en ville à travers le prisme des habitants. Cas de la ville de Sousse

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The impact of the COVID-19 pandemic on the health and well being on people's use and perception of Tunisian green spaces

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Friends and enemies agents collaboration protocol to optimize multi-skills patient scheduling in emergency department

International audienceThis paper focuses on scheduling patients in emergency department (ED) according to the priority of patients' treatments, determined by the triage process. This multiskills patient scheduling problem is modeled through four dimensional (hypercube) solutions search space whose axes are: Medical staff, Patients, ED structure and Time and it can be formulated as a flexible job shop scheduling problem. We have then to solve a NP-hard combinatorial optimization problem (COP) in the emergency department (ED). The objective is to minimize a score integrating the total waiting time of patients in the (ED) with emphasis on patients with severe conditions. The Friends and Enemies collaboration protocol between agents is developed for solving the problem where each agent integrate a complete metaheuristic scheme in its behavior. Each agent act autonomously in the solution environment and interacts cooperatively with it and with the other agents. The interaction between agents allows the metaheuristic hybridization including the tuning of its parameters. The simulation results show that the scenarios with 2 or more agents were significantly higher in performance than the scenarios with 1 single agent. Thus, it is confirmed that the collaboration protocol between agents influences the quality of the solutions and the scalability of our approach, with the addition of new agents, there is an improvement in the results. Our approach is tested on a set of real (ED) data and the simulation results show that the proposed friends end enemies collaboration protocol can significantly improve the efficiency of the (ED) by reducing the score and especially the total waiting time of multi-skills patient scheduling problem

Impact of the automation of inpatient bed management to reduce the emergency service waiting time

International audienceThe patient waiting time to be transferred for hospitalization is the time that the patient waits between the decision to hospitalize and the actual admission to an inpatient hospital bed. One of the difficulties encountered in qualifying waiting time for inpatient bed is the inability of hospital information systems to measure it. Hospitals in France have a specialized bed allocation team. This team must manage the bed allocation problem between different hospital departments using phone communication to assign patients to the adapted service. This kind of communication represents a lengthy additional workload in which effectiveness is uncertain. This paper presents a new approach to automate bed management in downstream service. For that, we have implemented algorithms based on artificial intelligent integrated in an inpatient web platform using IoT-Beacons, which is implemented to improve and facilitate the exchange of availability information of downstream beds within the Lille university hospital center (LUHC)

Generic agent-based optimization framework to solve combinatorial problems

International audienceThe aim of this paper is to describe our proposed ABOS framework (Agent-Based Optimization Systems) by demonstrating the interest in using the multi-agent approach while operating hybrid metaheuristics to solve Combinatorial Optimization Problems (COP). Two main contributions are highlighted in this work: 1) to show that the alliance of the multi-agent systems (MAS) and the metaheuristics, based on the interaction and the parallelisms concepts, facilitates the hybrid metaheuristics development and allows the simultaneous exploration of different regions of the search space and 2) to demonstrate that the use the multi-agent approach, in the context of optimization, is a crucial option in the process of hybridization allowing the development of generic structures. These later promote the interaction between metaheuristics independent of the problem to be addressed. Our challenge in this ABOS framework is to endow the participant agents, with a set of rational behaviours allowing them to change in real time their strategies, according to the optimization process evolution. The simulation results show that the collaborative optimization can be effective in some cases, hence the need to set effectively the parameters of the optimization algorithms behaviours and the collaborative protocols. We also demonstrate that the use of ABOS framework with MAS allows a more robust and generic structure, capable with minimal changes handling different COP