Applications of Software Agent Technology in the Health Care Domain
Abstract
Agent Cities [4] is an ambitious project whose main aim is the construction of a worldwide, publicly accessible network of agent-based FIPA platforms!. In March 2003 there are around 50 active platforms. Each of them supports agents that offer services similar to those that can be found in a real city (facilities, amenities, attractions, information and commercial services). It is expected that, in the near future, it will be possible to implement intelligent complex compound services (e.g. agents that are able to help a user to plan a weekend away, including tasks such as booking air tickets, selecting and booking a room in an appropriate hotel, buying tickets for the theatre and reserving a table in a restaurant that is near the theatre and offers the user's favourite meal). The main aim of our work was to develop a set of agents that could offer to the citizens and visitors of a city not the usual leisure-oriented services but health-care related services. This interest is related with previous work that our research group had done in the last years in the application of AI techniques (especially agent-based technology) to the medical domain (see e.g. [6]). Therefore, we decided to design and implement a multi-agent system with the following features: • The user may request information about all the medical centres available in a particular geographical area.
Chapters (9)
Agent technology has become a leading area of research in AI and computer science and the focus of a number of major initiatives. One of these is the AgentCities project funded by the European Union 5thFramework research programme(http:// www. agentcities. org). AgentCities is a federation of specialist communities with a common interest in agents, one of which is concerned with health care. In this book, John Nealon and Toni Moreno have brought together an interesting set of papers that discuss the many practical issues that arise in trying to build agent applications in medicine. The papers are not simply focused on the health care domain however: they also succeed in raising a number of theoretical issues of wide relevance to the general field of agent research
Previous work at Oxford Brookes University developed a system to advise on diabetes treatment that enabled data to be displayed according to the choices of each user. Due to the time critical nature of the problem, spending time searching through the data was not feasible. This reduced the usefulness of the system in the clinical setting for which it was designed. Thus a more automated approach was required. A multiagent system has been utilised to drive the adaptivity. A set of simple agents, each concerned with a single aspect of the system, communicate with each other and the suggested summary is a result of the emergent behaviour of the whole system. While emergent behaviour is used in other areas where agents have been applied, notably robotics, it is novel to use this approach in adaptive interfaces. This paper first considers the use of reactive agents to provide a context for the application of emergence in the area of self-adaptive interfaces. The field of adaptive interfaces is also considered to identify approaches that have been used in the past. An emergent multiagent system using a two-layer model is then described. This approach has been applied and tested to the problem of providing selfadaptivity at the interface to allow for decision support to be delivered in real-time for a clinician to employ
In this paper we describe an application called GruSMA1 which is an agent-based system that provides medical services to its users (the citizens or the visitors of a city). This multi-agent system contains agents that have information about the medical centres, departments and doctors of the city. All these agents coordinate their tasks to provide a set of services to the user of the system, who can search for medical centres satisfying a given set of requirements, as well as access his/her medical record, or make a booking to be visited by a particular kind of doctor. Special care has been paid to the definition of a medical ontology and to the implementation of mechanisms that guarantee the confidentiality in the access and transmission of medical data
Agent.Hospital is an open agent-based (software) framework for distributed applications in the healthcare domain. Previous appropriation of the Agent.Hospital development is the application and examination of agent technology in a realistic business scenarios and the identification of further research needs. This paper introduces the framework developed by the German Priority Research Program 1083. We describe the initial system concept, currently implemented or specified functionalities and the integration of FIPA standardization activities. The example scenario, “clinical trials”, illustrates how Agent.Hospital supports distributed clinical processes as well as further research of agent technology
Modern healthcare specialists are overwhelmed with medical information available on the Internet. However, it is difficult to find a particular piece of information when and where they actually need it. The National electronic Library for Health (NeLH) is addressing this issue by providing a single-entry portal to evidence-based medical information on the Internet enhanced with a quality tag assigned by professional experts in the field. In order to fully utilize the potential of an Internet-based library, the NeLH is distributed and consists of a number of Virtual Branch Libraries (VBLs), each dedicated to a particular disease or a medical area. Our team is responsible for the development of the communicable disease branch of the NeLH, called NeLCD (National electronic Library for Communicable Disease). VBLs are dynamically updated and their design reflects the needs of each particular user base. However, users accessing a single VBL may want to search the entire NeLH or should have the option of being able to search the entire NeLH. Therefore, support for a distributed search according to an adopted topology of VBL servers is essential. Intelligent Interface Agents are essential for the development and runtime of the library as they perform autonomously a number of tasks related to the search, assist humans in information publishing, the document review process and data exchange and retrieval. In this paper, we present an agent-based solution to assist in distributed search across the NeLH, and customization and personalization in the NeLCD.
This ongoing R&D project is about design of a peer-to-peer groupware aimed to support wound care documentation. Nurses decentralized control is recognized as a crucial factor for transition from a single paper print form to a distributed electronic case book. The use of digital photographs has been introduced revealing a developmental potential in this Swedish municipal elder care. In an evolutionary design approach the project is trying to accomplish a mapping between the work activity and the core features of the software system. An authorization layered model and peer membership rules are suggested and elaborated in the design work as key elements in a developed peer-to-peer network architecture. The article reports on utilizing ethnographic field studies and nurses participatory design work as contributions to the software development. The opportunity of improving work and learning by means of the peer-to-peer environment is discussed as additional aspects of software development work
This report describes the development of a teaching environment that uses agents to support learning. An Intelligent Tutoring System will be described, that guides students during learning. This system is meant for nurse education in the first place, but it is generic in the sense that the core is separated from the exercise modules and user interfaces. This means that the system can also be used for other (non-nursing) exercises. Exercises can be provided to the system in the form of XML data-files. A user interface can be text-based or 2D, but it can also be a 3D virtual reality environment. An application of the teaching environment for nurse training is described.
The use of Multi-Agent Systems (MAS) in health-care domains is increasing. Such Agent-mediated Medical Systems are designed to manage complex tasks and have the potential to adapt gracefully to unexpected events. However, in this kind of system issues of privacy, security and trust are particularly sensitive and real deployment of Agent technologies in such domains must meet high standards in each of these areas. This paper outlines a existing prototype Agent—Based application in the Organ and Tissue transplant domain and outlines some of the challenges in the area of security which have been identified in moving it from prototype to real usage
This paper presents a multi-agent support system for the different stages of an organ transplant process. With the automatisation of certain tasks in this complex coordination process, we can decrease the time spent before the organ reaches the medical centre of the receiver. This is very important because organs degrade very quickly and they cannot be frozen. The paper explains how we can improve the: searching of possible receivers, the selection of the most appropriate candidate, the transport of the organ and the scheduling of the surgical operation. The design of this prototype has been done according to the Spanish Model of transplant co-ordination
... Digital agents are computers that undertake tasks previously performed by humans. As such, they function autonomously, react to environmental situations, initiate actions, communicate with humans or machines, and behave intelligently [33]. An increasing volume of digitized data, improved algorithms, and better hardware has vastly enhanced the range of tasks that digital agents can perform. ...
... Discussion about the capabilities of digital agents and their suitability has also reached the medical domain [33,39,40]. Conceptually, the dyadic physician-patient consultation becomes triadic [41][42][43][44] if a digital agent is included. ...
... Digital experts reveal their capabilities during the consultation by integrating and extending the functionalities of EMRs and encounter PDAs with the characteristics of digital agents [33]. These include autonomous and intelligent behavior, reactions to environmental situations, and communication with humans or machines. ...
Background
Physicians are currently overwhelmed by administrative tasks and spend very little time in consultations with patients, which hampers health literacy, shared decision-making, and treatment adherence.
Objective
This study aims to examine whether digital agents constructed using fast-evolving generative artificial intelligence, such as ChatGPT, have the potential to improve consultations, adherence to treatment, and health literacy. We interviewed patients and physicians to obtain their opinions about 3 digital agents—a silent digital expert, a communicative digital expert, and a digital companion (DC).
Methods
We conducted in-depth interviews with 25 patients and 22 physicians from a purposeful sample, with the patients having a wide age range and coming from different educational backgrounds and the physicians having different medical specialties. Transcripts of the interviews were deductively coded using MAXQDA (VERBI Software GmbH) and then summarized according to code and interview before being clustered for interpretation.
Results
Statements from patients and physicians were categorized according to three consultation phases: (1) silent and communicative digital experts that are part of the consultation, (2) digital experts that hand over to a DC, and (3) DCs that support patients in the period between consultations. Overall, patients and physicians were open to these forms of digital support but had reservations about all 3 agents.
Conclusions
Ultimately, we derived 9 requirements for designing digital agents to support consultations, treatment adherence, and health literacy based on the literature and our qualitative findings.
... On the one hand, the healthcare service system under NHI grants patients the freedom to access any medical services covered by NHI policy, which makes it difficult for individual medical service providers to manage patients' health. On the other hand, since Taiwan's national health policy is a single payer system, diagnosis and treatment information is centrally collected for reimbursement purposes, which enables the NHI to assess healthcare ecosystems in a timely manner (Nealon & Moreno, 2003). Therefore, the up-to-date healthcare information provided by NHI is useful for planning and conducting capitation projects. ...
... In an intelligent agent setting, three capabilities have to be included in the system design: reactivity, proactiveness, and social ability (Xiang, 2002). A multi-agent system can be defined as a collection of autonomous agents which coordinate activities in order to collectively solve problems that cannot be tackled by an agent individually (Nealon & Moreno, 2003). ...
... The previous research on multi-agent systems provides a novel tool for simulating societies, shedding light on various kinds of social processes. Nealon and Moreno (2003) point out that many different kinds of problems in the healthcare domain, such as patient scheduling, organ and tissue transplant management, community care, information access, decision support systems, training, internal hospital tasks, and senior citizen care, have already been dealt with using intelligent agents. Moreno (2003) reported on the AgentCities.NET European project, which involved the construction of a worldwide network of agent-based platforms, called AgentCities, by a number of working 6 groups (Moreno, Isern, & Sánchez, 2003;Nealon, 2013). ...
... Consequently, evolving technological developments and the need to adopt technology in health care during the COVID-19 pandemic have led to increasing interest in how wearable devices can be used in health care. Wearable devices have shown the potential to improve clinical outcomes, as the personalized information provided by wearable devices has resulted in more timely action, informed decision-making, and multidisciplinary collaboration [26][27][28]. ...
Background:
The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.
Objective:
In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.
Methods:
A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.
Results:
Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.
Conclusions:
This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
... First, it provides a collaborative working environment where several agents work together to deliver the right decision at the right time to a remote user/machine ( Figure 3), i.e., the agent shares the workload with user/machine by means of the integrity and reliability of the decision taken for a particular procedure. This consensus algorithm ensures that the decision is not tampered [7], and additionally, the decision is safe not to involve outside or outside parties in its making. Second, the proposed consensus algorithm is included in the processes of resource division based on multi-agent systems [8]. ...
Accord is an important operation in the mechanism design for multi-agent systems and consequently to applications built for these environments , such as ad hoc networks, virtual organizations, and decision-support tools for smart production systems. Much attention has been given to designing the process for resisting manipulation by strategic voting and decentralized control. The emphasis is on local decision-making based on locally available information and perception of events occurring locally. This manuscript presents a secure decision-support algorithm in a multi-agent system. The proposed consensus-based model serves multiple purposes in production planning and control, supply chain management , and product design and development. In particular, we describe decision-making, the selection of a leader, and the exploitation of limited resources. The importance of the proposal is to ensure robustness in a specific way; in which, decisions are carried out considering that the system is open, which means that the number of agents present is variable at each process. Our model is based on that a group of agents makes different decisions in a task, then the algorithm chooses one of these decisions logically, safe, efficient, and fast and stays away from emotional decisions even in dealing with the decisions of others that affect production.
... Le système de santé est caractérisé par une prise de décision et une gestion partagée et distribuée de soins, ce qui nécessite une communication complexe et diversifiée entre les professionnels soignants (Nealon 2003). L'utilisation des entités autonomes, intelligentes, pro-actives et collaboratives qui interagissent dans un environnement distribué, pourrait apporter de l'aide à la décision efficace pour les problèmes hospitaliers. ...
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-urgences
Mainly as a consequence of societal changes, the traditional approach to provide cares and social services in hospitals and institutional centres is being paralleled with a growing tendency to provide cares in the community, directly. Information and communication technologies play an important role in enabling and supporting this tendency. However, the most relevant requirements for this application area require a choice of adequate technologies. In particular, multi-agent systems are being proposed as a suitable technology for this application area, since they are based on loosely coupled and heterogeneous components, allow the dynamic and distributed management of data, and support the remote collaboration among users. In fact, multi-agent systems are being effectively used in a number of International projects related to health and community care, which demonstrate the advantages, together with the open challenges characterizing this approach.
The successful use of intelligent agents in healthcare has attracted researchers to apply this emerging software engineering paradigm in more advanced and complex applications. Main success factor is the natural mapping of real world medical problems into cyber world. Multi-agent architecture can easily model the heterogeneous, distributed and autonomous health care systems. The multi agent systems have been applied from single healthcare activity like knowledge based medical system to complex, multi-component based systems like complete healthcare unit. The use of multi agent systems in health care domain has also opened the ways to find out new applications like personalized and socialized health care systems. This versatile use of multi agent systems has also posed new problems for researchers like; security, communication, and different social issues. This work reviews recent years’ research and applications of multi agent systems in healthcare published in different research journals, international conferences, and implemented practically. We reviewed five subdomains and three systems in each subdomain. A set of common parameters of these systems has been extracted and compared to analyze systems’ merits and deficiencies. Based on our analysis, we have provided recommendations for multi agent systems applied in healthcare domain. Future research directions for interested researchers and practitioners are also discussed. As our own future research work, we intend to study healthcare and multi agent systems in e-commerce.
The healthcare system that prevailed some years ago was a mere pen and paper based system. A number of workers, staff, and written records were the main components of the prevailing system of healthcare. This had a number of drawbacks, and a number of mishaps occurred due to mismanagement of data and information. There was a need for development. Then, the concept of telemedicine came, which revolutionized the healthcare paradigm to a great extent. With the advancement of telemedicine, many major problems of the prevailing system were removed. But, still there were many other aspects which could be further improved to make healthcare facilities more enhanced. Keeping this in mind, the concept of Multi Agent System (MAS) was introduced in the healthcare system later. MASes are considered as the best and most appropriate technology that can be used in the development of applications in healthcare paradigm where the presence of multiple agents, heterogeneous and loosely coupled components, the data management in a dynamic and distributed environment, and multi-user collaborations are considered the most pertinent requirements for healthcare system. This chapter focuses mainly about MAS, its applications, and some systems that were developed by the authors.
Ambient Assisted Living (AAL) is an emergent area that provides useful mechanisms that allows tracking elders through sensoring, for example, using mobile devices. It is necessary a permanent attention to these people by caregiv-ers and a growing necessity to create mechanism to support this task. In our proposal, we have a network of caregivers that work together to ensure the wellbeing of elders in their daily activities. This paper aims to model the interaction process of different actors with SafeRoute, an AAL system that pretends monitoring elders in their day-to-day daily living activities in outdoor environments. SafeRoute merges sensor data provided by different sensors built-in mobile devices to provide alert mechanisms for caregivers. We additionally present an upper context ontology to represent general concepts of our context, visualizing the proposed interaction model.
This article presents three successful case studies oriented to disabled and dependent people. These three case studies are the results of the following corresponding projects: Autonomous aGent for monitoring ALZheimer’s patients (AGALZ), which facilitates the monitoring and tracking of patients with Alzheimer’s; AZTECA, which is formed by a set of tools that facilitate the work of disabled people in their work environment; and MOVI-MAS, which simulates a 3D work environment enabling the detection of dangerous situations. These tools were developed using an agent platform called PANGEA, which is a platform to develop open multiagent systems, specifically those including organizational aspects such as virtual agent organizations.
New technologies have become an important support for the monitoring of older people in outdoor environments by their caregivers. Smart phones equipped with a rich set of powerful sensors allowed the ubiquitous human activity recognition on mobile platforms at a low cost. Ambient Intelligence (AmI) is an emergent area that provides useful mechanisms that allows tracking elderly people through opportunistic sensoring using smartphone devices. This paper aims to show the second version of SafeRoute, an AmI system that fusions geo-localization sensors data embedded in smartphone devices for the monitoring of elderly people. This version improves functionalities of the previous one with the inclusion of new ones in the two components of this system: the Android OS application CareofMe and the web system SafeRoute. The proposed system merges localization data from GPS and Wifi sensors data in Android OS and includes the use of GoogleMaps functionalities in Android OS and web environments for provide alerts for caregivers.
The necessity of using new technologies to monitoring elderly people in open-air environments by their caregivers has become a priority in the last years. In this direction, Ambient Intelligence (AmI) provides useful mechanisms and the geo-localization technologies embedded in smartphones allows tracking elderly people through opportunistic sensoring. The aim of this paper is to show a practical example to how to combine some technologies for monitoring elderly people through the system SafeRoute. We describe the two components of this system: the Android application CareofMe and the web system SafeRoute. The proposed system uses GPS, Wifi and accelerometer sensoring, GoogleMaps functionalities in Android and web environments and an alert system for caregivers.
One of the most rapidly evolving government sectors in any country is the healthcare industry. Today, the healthcare system of developed nations is facing the various challenges presented by an aging population. Increasing demand for health services presents a challenge for the existing healthcare industry, as vast amounts of resources will be required. The Multi-Agent System (MAS) approach provides a powerful platform for modeling and solving real world problems such as healthcare. This makes it possible for patients to remain at home and consequently reducing costs. This paper presents developed MAS applications in healthcare, as well as explores the future of developed MAS applications. In particular, it will be argued that these health based MAS applications can provide a reasonable way to mitigate the cost due to increased demand for services.
Though multi-agent systems have been explored in a wide variety of medical settings, their role at the emergency department care level has been relatively little investigated. In this paper, we propose a tool to assist decision-making process for the care of patients at the emergency department. This tool aims to improve the quality of care within the emergency departments with rapid access to pertinent data, integration of care's protocols and assures knowledge of the quantity and the quality of medical activity. This multi-agent model was adopted to define the behavior of entities by distributing data and tasks in an attempt to explain and predict events in the emergency department. We have chosen to build intelligent agents that perform coordination tasks for the users, i.e. the medical staff. To solve some problems, the agents have to cooperate. To ensure this cooperation, the system uses an agent interaction protocol making it possible to accelerate the process of task allocation.
Various areas of the healthcare industry have seen some progress in the use of ICT to aid in the treatment of patients including some amount of automation in the key areas. The authors infer that some pitfalls exist in the healthcare administration. To obviate these, the use of Agent technology as a practical solution to solve some hospital related issues with particular emphasis on hospital search and appointment allocation has been researched. The authors explore the use of agent technology to assist patients seeking treatment for their ailment at a hospital chosen, and priority is provided based on treatment. The patients have been given the facility to select an appropriate hospital based on their preferred selection criterion, achieved through the use of their mobile phone by exactly replicating the job of a human being agent. The authors study first how smart agents can be used to search for hospitals based on user selection criteria in combination with the Google Map facility and fairly diagnose their medical ailments using Layman language. The Agent proposed here, also facilitates verification of doctor's license before the appointment with him is confirmed. Lastly, the proposed system provides the rating and the popularity factors of the selected hospital in respect of ailment based and on number of persons visited. The system also alerts the hospital for poor rating/popularity enabling working towards improvement. The Smart agents proposed for action act autonomously and have the ability to intelligently use the available knowledgebase and user parameters to make appropriate decisions on behalf of the user. The system uses ANDROID 2.2, JADE-LEAP and the Google API to provide a robust, user friendly solution.
Patient care is becoming increasingly complex and multidisciplinary for many conditions, notably cancer and chronic diseases, in which a care team participates in and shares responsibility for the patient's care. Providing IT support for joint clinical decision making in an open and distributed environment raises some challenges that are worth our attention: 1) new clinical evidence and guidelines, published by healthcare authorities and subject to continuous revision, need to be shared and enacted by the care team, as automatically as possible, 2) clinical specialists, located in their own working environments, need to be able to group together wherever necessary, 3) decision points, distributed in the environment, need to refer consistently the same set of guidelines and unless these are well-coordinated across the care team, safe delivery of care will be hard to guarantee. In this paper we propose an open and adaptive agent architectural model to resolve these challenges. This is based on an Agent-oriented Model Driven Architecture and a decision support management model, which are integrated to support joint clinical decision-making.
In this chapter, we will discuss some advantages of ontology- and agent-based systems. Ontologies provide machine readable
and understandable domain knowledge and play an important role in knowledge representation, sharing and management, data semantics,
intelligent information retrieval, mediation, natural language applications, and the like. Two important characteristics of
agents are their autonomous and collaborative behaviour and, as such, agent-based systems are used to support distributed
computing, dynamic information retrieval, automated service discovery, computational intelligence etc. We also describe the
advantages of the integrated approach i.e. ontology-based multi-agent systems. Ontology- and agent-based computing are two
different but complementary technologies; ontologies give intelligence to the system while the agents provide the system dynamics.
At present, many medical applications are limited to storing basic information concerning patients, such as, their medical
history, diagnostic issued by specialists, reviews, etc. But the medical work is not easy when considering complex cases involving
different experts and require a high degree of coordination and cooperation. Traditionally communication between experts takes
place across consultations involving large amounts of resources (money, time, etc.). MultiAgent Systems (MAS) are a branch
of artificial intelligence distributed to adequately address problems of coordination and communication (among others) where
several decisionmaking agents are involved. The objective of this work is the design and implementation of MAS that allows
coordinate the workflow of a medical center. We have implemented a management tool for medical records and requests for medical
services based on Web technology. The application made available to doctors all the information necessary for carrying out
timely diagnosis, through electronic medical records. Regarding the tasks of coordination mentioned: assigning tasks to doctors,
requests for reviews, delivering results, among others, using technologies such as text messaging, e-mail messages and on-line.
To design agents and their roles, tasks and plans using the “Software Agent Architecture Pattern” the which is based on a
set of classes organized for the encoding software components with agents that are integrated into Web applications, in addition
to use as MAS commonKADS Methodology development of MAS. Currently have an application robust and stable. It is worth mentioning
that this approach used specifically in the medical field is extended to other areas that require coordination distributed.
This paper concentrates on the decision process based on multi-agent system theory; the holonic paradigm, and swarm intelligence techniques, Bayesian probability since, among others, uncertainty is an inherent feature of a medical diagnostic process with highly reliable results. The presented approach focuses on reaching the optimal medical diagnosis with the minimum risk under the given uncertainty. Additional factors that play an important role are the required time for the decision process and the produced costs.
Biomedical technology is a valuable asset of healthcare facilities. It is now universally accepted that, to assure patient safety, medical devices must be correctly managed and used, and that the quality of healthcare delivery is related to the suitability of the available technology. The activities that guarantee a proper management are carried on by the people working on a Clinical Engineering (CE) Department. In the chapter we describe a model to estimate the number of clinical engineers and biomedical equipment technicians (BMET) that will constitute the Clinical engineering department staff. The model is based on the activities to be simulated, the characteristics of the healthcare facility, and the experience of human resources. Our model is an important tool to be used to start a Clinical engineering department or to evaluate the performances of an existing one. It was used by managers of Regione Piemonte to start a regional network of Clinical engineering departments
The concept of provenance is already well understood in the study of fine art where it refers to the trusted, documented history of some work of art.
Given that documented history, the object attains an authority that allows scholars to understand and appreciate its importance
and context relative to other works of art. This same concept of provenance may also be applied to data and information generated
within a computer system; particularly when the information is subject to regulatory control over an extended period of time.
Today’s distributed architectures (not only Agent technologies, but also Web Services’ and GRID architectures) suffer from
limitations, such as lack of mechanisms to trace results. Provenance enables users to trace how a particular result has been
arrived at by identifying the individual and aggregated services that produced a particular output. In this chapter we present
the main results of the EU PROVENANCE project and how these can be valuable in agent-mediated healthcare applications. For
the latter we describe the Organ Transplant Management Application (OTMA), one of the demonstrator applications developed.
Agent technology has become a leading area of research in AI and computer science and the focus of a number of major initiatives [5]. The interest in applying Artificial Intelligence technologies first, and now Agent Technology to Healthcare has been a
growing one. From the very seminal and inspiring work as the one of Huang et al. [1] and [2] the use of agents in Healthcare has been continuously evolving and covering more aspects. Intelligent Agents are normally
used to observe the current situation and knowledge base, and then support the expert’s decision-making on an action consistent
with the domain they are in, and finally perform the execution of that action on the environment. This evolution brought the
creation of steady series of workshops where a growing community has been joining to put together the latest advancements
in the field see, for example [7, 8, 5, 6] and also major AI journals devote special issues to this field as for example [4, 3].
Fast, reliable, and correct medical diagnostics is of utter importance in today’s world where diseases can spread quickly. For this reason, we have developed a medical diagnosis system that is based on multi agent system theory, the holonic paradigm, and swarm intelligence techniques. More specifically, a huge number of comparatively simple agents form the basis of our system. In order to provide a solid medical diagnosis always a set of relevant agents needs to work together. These agents will provide a huge set of possible solutions, which need to be evaluated in order to conclude. The paradigm of swarm intelligence implies that a set of comparatively simple entities produces sophisticated and highly reliable results. In our scenario, it means that our agents are not provided with a real world model; i.e., it has only a very limited understanding on health issues and the process of medical diagnosis. This puts a huge burden on the decision process.
This paper concentrate on the decision process within our system and will present our ideas, which are based on decision theory, and here, especially, on Bayesian probability since, among others, uncertainty is inherent feature of a medical diagnosis process. The presented approach focuses on reaching the optimal medical diagnosis with the minimum risk under the given uncertainty. Additional factors that play an important role are the required time for the decision process and the produced costs.
This paper presents a case-based reasoning system developed to generate an efficient and proactive ambient intelligent application.
Ambient Intelligence (AmI) proposes a new way to interact between people and technology, where this last one is adapted to
individuals and their context (Friedewald and Da Costa 2003). The objective of Ambient Intelligence is to develop intelligent
and intuitive systems and interfaces capable to recognize and respond to the user’s necessities in a ubiquitous way, providing
capabilities for ubiquitous computation and communication, considering people in the centre of the development, and creating
technologically complex environments in medical, domestic, academic, etc. fields (Susperregui et al. 2004). Ambient Intelligence requires new ways for developing intelligent and intuitive systems and interfaces, capable to
recognize and respond to the user’s necessities in a ubiquitous way, providing capabilities for ubiquitous computation and
communication. The multi-agent systems (Wooldridge and Jennings 1995) have become increasingly relevant for developing distributed
and dynamic intelligent environments. A case-based reasoning system (Aamodt and Plaza 1994) has been embedded within a deliberative
agent and allows it to respond to events, to take the initiative according to its goals, to communicate with other agents,
to interact with users, and to make use of past experiences to find the best plans to achieve goals. The deliberative agent
works with the concepts of Belief, Desire, Intention (BDI) (Bratman 1987), and has learning and adaptation capabilities, which
facilitates its work in dynamic environment.
This paper presents an autonomous intelligent agent with a human thinking reasoning model, based on past experiences. The
agent is developed to assist medical staff in geriatric residences. The health care process is a vital function, requiring
nature-inspired solutions imitating the residence staff behaviours. An autonomous deliberative Case-Based Planner agent, AGALZ
(Autonomous aGent for monitoring ALZheimer patients), is developed and integrated into an environment-aware multi-agent system,
named ALZ-MAS (ALZheimer Multi-Agent System), to optimize health care in geriatric residences. ALZ-MAS is capable of obtaining
information about the environment through RFID technology.
We propose software architecture to monitor elderly people in their own homes. We want to build patterns of monitored people
dynamically from data about activity, movements and physiological information. To obtain this macroscopic view, we use a multi-agent
method of classification: every agent has a simple skill of classification. They generate partial partitions and cooperate
to obtain a set of patterns. The patterns are used at a personal level, for example to raise an alert, but also to evaluate
global risks. These data are dynamic; the system has to maintain the built patterns and has to create new patterns. Therefore,
the system is adaptive and can be spread on a large scale.
We propose an agent-based architecture to build classifications from evolutionary and distributed data. In the process, each
agent builds a partial classification based on its data and a complete classification is then constructed from the partial
ones by comparing them. Agents can collaborate by employing a restricted cooperation protocol. We present an application in
the e-health domain, where global behavior patterns are built from activity or physiological data related to elderly people
monitored at home by a variety of sensors. A step-by-step example illustrates our proposition.
Cancer research has become an extremely data rich environment, with huge batteries of tests performed to quantify and categorise tumours. Multiple analyses of biochemical, molecular and immunohistological markers on tissue samples generate large and complex data sets to compare with clinical and pathological parameters. The manual data analysis procedures by scientists have become impractical and automation is becoming the only method for complete and comprehensive analysis of the search space. To this end, a multi-agent system has been developed to automate the data analysis process. Project agents are tasked with overseeing the analysis. Initially, they create a list of hypotheses based on all parameters associated with the project. Agents then commence data collection and aggregation by directly interfacing with the various data sources. Conventional statistical tests can then be performed under agent control to determine the significance of these hypotheses. The final stage is to present collated results online. INSPIRE uses automated data retrieval and analysis on large and diverse cancer datasets to allow for the quick identification of significant results amongst the noise of the large dataset. This results in a more streamlined research process, which makes large cohort, multivariate projects easier to manage in a secure, user-friendly, web-based data management system.
Access to the correct healthcare facility is a major concern for most people, many of whom gather information about the existing hospitals and healthcare facilities in their locality. After gathering such information, people must do a comparison of the information, make a selection, and then make an appointment with the concerned doctor. The time spent for this purpose would be a major constraint for many individuals. Research is currently underway in this area on incorporating Information and Communication Technology (ICT) to improve the services available in the health industry. This paper proposes an agent based approach to replicate the same search operations as the individual would otherwise do, by employing an intelligent agent. The proposed agent based system has been simulated and also validated through implementation on an individual’s smart phone or a PDA using JADE-LEAP agent development kit.
The objective of this paper is to present a model that uses multi-agent system (MAS) technology to assist doctors based at rural areas to receive timely expert opinions from specialist doctors working in medical institutions like the Postgraduate Institute of Medical Research, Chandigarh or the All India Institute of Medical Sciences, New Delhi.
A MAS is a software entity wherein agent metaphor is used to represent human-like intelligence and behavior. These agents are distributed over geographical areas and are part of software running at these sites. The foundation for this model lies in the fact that medical intelligence is distributed in nature and, if it is possible to bring this knowledge together, it could prove to be advantageous in providing improved health care to those people in rural areas. A few existing research works are included to demonstrate the usage of MAS in health care.
It has been observed that the health scenario in India is ranked poor world-wide by international organizations and, with the increasing population, this will worsen.
Initially, the aim is to concentrate on a few medical departments only. It can be extended into more detailed framework.
Physical computing and networking infrastructure are required for practical implementation along with training for the doctors.
The primary beneficiaries of this system are likely to be those patients in the rural sector requiring expert medical opinion. The government health organizations will be able to provide quality medical facilities comparable with the medical facilities available in the developed nations. Currently this would seem to be a distant dream.
This article is part of a special issue on Intelligent Agents in Healthcare. Agent technology is currently being applied to solve different kinds of problems in the healthcare domain. This special issue presents six articles that discuss different applications, in areas as diverse as automatic e-health service discovery, use of argumentation techniques in organ transplants, and the implementation of agent-based mechanisms that ensure the confidentiality and privacy of sensitive medical data.
People are increasingly cooperating to share electronic information and techniques throughout various industries. In healthcare applications, data (a single patient's healthcare history), workflow (procedures carried out on that patient), and logs (a recording of meaningful procedural events) are often distributed among several heterogeneous and autonomous information systems. Understanding a patient's treatment history can help healthcare providers make treatment decisions. Provenance-aware applications can facilitate this process by tracing events, event dependencies, and provider decisions across various healthcare institutions
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