pubs2024

@inproceedings{Chaves2024195,

title = {Collaborative Platform for Intelligent Monitoring of Diabetic Foot Patients - Colab4IMDF},

author = {A. Chaves and R. Sousa and J. Machado and A. Abelha and H. Peixoto},

editor = {Ferras C. Diez J.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187779099&doi=10.1007%2f978-3-031-54256-5_18&partnerID=40&md5=7cc7dae3a967fb508b0024598e23f8e6},

doi = {10.1007/978-3-031-54256-5_18},

issn = {23673370},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {933 LNNS},

pages = {195-204},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This research work aims to set the basis for the development of a multidisciplinary platform designed to assist healthcare professionals, their institutions and more important, diabetic patients to manage diabetic foot syndrome complications. The system’s primary function is the automated classification of diabetic foot ulcers (DFU), with the ultimate goal of expediting treatment and improving patient well-being. Given the sporadic nature of diabetic patients’ medical consultations, the time gaps between appointments can exacerbate symptoms. To address this issue, the project advocates for sustained communication between physicians and patients, facilitated by the exchange of patient images, thereby reducing the need for frequent in-person visits. This manuscript shows project’s initial phase that involves the research and modelling of an architecture that ranges from mHealth to Deep Learning algorithm capable of rapidly and accurately classifying user-submitted images, offering clinical decision support and autonomous identification of potential DFU complications from the patient’s perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},

keywords = {Clinical research; Decision support systems; Deep learning; mHealth; Patient treatment, Collaborative platform; Diabetic foot; Diabetic foot ulcer; Diabetic foot wound follow-up; Diabetics patients; Follow up; Health information systems; Healthcare standards; Microservice; Multi agent, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chaves2023230,

title = {Intelligent Systems in Healthcare: An Architecture Proposal},

author = {A. Chaves and L. Montenegro and H. Peixoto and A. Abelha and L. Gomes and J. Machado},

editor = {Hornos M. J. Julian Inglada V. Novais P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174435986&doi=10.1007%2f978-3-031-43461-7_23&partnerID=40&md5=f1fab121626cc690c7357a2d9bd721ba},

doi = {10.1007/978-3-031-43461-7_23},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {770 LNNS},

pages = {230-238},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Multi-Agent Systems has existed for decades and has focused on principles such as loose coupling, distribution, reactivity, and local state. Despite substantial tool and programming language research and development, industry adoption of these systems has been restricted, particularly in the healthcare arena. Artificial intelligence, on the other hand, entails developing computer systems that can execute tasks that normally require human intelligence, such as decision-making, problem-solving, and learning. The goal of this article is to develop and implement an architecture that includes multi-agent systems with microservices, leveraging the capabilities of both methodologies in order to harness the power of Artificial Intelligence in the healthcare industry. It assesses the proposed architecture’s merits and downsides, as well as its relevance to various healthcare use cases and the influence on system scalability, adaptability, and maintainability. Indeed, the proposed architecture is capable of meeting the objectives while maintaining scalability, flexibility, and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},

note = {cited By 1; Conference of 14th International Symposium on Ambient Intelligence, ISAmI 2023 ; Conference Date: 12 July 2023 Through 14 July 2023; Conference Code:301589},

keywords = {Coupling distribution; Development industry; Intelligent healthcare; Local state; Loose couplings; Microservice; Programming language researches; Proposed architectures; Reactivity state; Research and development, Decision making; Health care; Intelligent agents; Intelligent systems; Scalability, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Coelho2023673,

title = {Multi-agent System for Multimodal Machine Learning Object Detection},

author = {E. Coelho and N. Pimenta and H. Peixoto and D. Durães and P. Melo-Pinto and V. Alves and L. Bandeira and J. Machado and P. Novais},

editor = {Martinez de Pison F. J. Perez Garcia H. Garcia Bringas P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172230717&doi=10.1007%2f978-3-031-40725-3_57&partnerID=40&md5=79d45a455c79da96394a5b0e05a56908},

doi = {10.1007/978-3-031-40725-3_57},

issn = {03029743},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {14001 LNAI},

pages = {673-681},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Multi-agent systems have shown great promise in addressing complex problems that traditional single-agent approaches are not be able to handle. In this article, we propose a multi-agent system for the conception of a multimodal machine learning problem on edge devices. Our architecture leverages docker containers to encapsulate knowledge in the form of models and processes, enabling easy management of the system. Communication between agents is facilitated by Message Queuing Telemetry Transport, a lightweight messaging protocol ideal for Internet of Things and edge computing environments. Additionally, we highlight the significance of object detection in our proposed system, which is a crucial component of many multimodal machine learning tasks, by enabling the identification and localization of objects within diverse data modalities. In this manuscript an overall architecture description is performed, discussing the role of each agent and the communication protocol between them. The proposed system offers a general approach to multimodal machine learning problems on edge devices, demonstrating the advantages of multi-agent systems in handling complex and dynamic environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of Proceedings of the 18th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2023 ; Conference Date: 5 September 2023 Through 7 September 2023; Conference Code:299919},

keywords = {Agent approach; Complex problems; Learning objects; Machine learning problem; Machine-learning; Multi-modal; Multi-modality; Multimodal machine learning; Objects detection; Single-agent, Internet protocols; Machine learning; Network architecture; Object detection; Object recognition, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sousa2023347,

title = {Implementing a Software-as-a-Service Strategy in Healthcare Workflows},

author = {R. Sousa and H. Peixoto and A. Abelha and J. Machado},

editor = {Analide C. Sitek P. Ossowski S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172215447&doi=10.1007%2f978-3-031-38333-5_35&partnerID=40&md5=65351fd6a4cb540be0182fe1e9567940},

doi = {10.1007/978-3-031-38333-5_35},

issn = {23673370},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Networks and Systems},

volume = {740 LNNS},

pages = {347-356},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The spread of healthcare technology has resulted in a massive amount of data, particularly in the form of laboratory test results, which play an important role in medical diagnosis and treatment. However, managing and interpreting such large amounts of data has proven increasingly difficult, particularly for resource-constrained healthcare facilities. To address this issue, we present a multi-agent system for effective laboratory test result management based on Software-as-a-Service (SaaS) technology. This paper contains a case study that evaluates the system’s performance and efficacy. The study’s goal is to examine the viability of using a multi-agent system and SaaS technology to manage laboratory test data, highlighting the system’s advantages over conventional alternatives. In the age of big data, the deployment of this system could dramatically improve healthcare service efficiency, quality, and cost-effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023 ; Conference Date: 12 July 2023 Through 14 July 2023; Conference Code:298499},

keywords = {Big data; Cost effectiveness; Diagnosis; Health care; Information management; Real time systems; Software agents; Software as a service (SaaS); Software testing; Web services, Case-studies; Cloud paradigm; Healthcare facility; Healthcare technology; Healthcare workflow; Laboratory test; Large amounts of data; Real-time information systems; Service strategy; Software-as-a- Service (SaaS), Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cardoso2014127,

title = {A multi-agent platform for hospital interoperability},

author = {L. Cardoso and F. Marins and F. Portela and M. Santos and A. Abelha and J. Machado},

editor = {Ramos C. Novais P. Nihan C.E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927780765&doi=10.1007%2f978-3-319-07596-9_14&partnerID=40&md5=a466f6e749d887c596eeef2dd454bc93},

doi = {10.1007/978-3-319-07596-9_14},

issn = {21945357},

year  = {2014},

date = {2014-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {291},

pages = {127-134},

publisher = {Springer Verlag},

abstract = {The interoperability among the Health Information Systems is a natural demand nowadays. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is a Multi-Agent System (MAS) specifically developed to guarantee interoperability in health organizations. This paper presents the Biomedical Multi-agent Platform for Interoperability (BMaPI) integrated in AIDA and it is used by all hospital services which communicates with AIDA, one of the examples is the Intensive Care Unit. The BMaPI main objective is to facilitate the communication among the agents of a MAS. It also assists the interaction between humans and agents through an interface that allows the administrators to create new agents easily and to monitor their activities in real time. Due to the BMaPI characteristics it is possible ensure the continuous work of the AIDA agents associated to INTCare system. The BMaPI was installed in Centro Hospitalar do Porto successfully, increasing the functionality and overall usability of AIDA platform. © Springer International Publishing Switzerland 2014.},

note = {cited By 10; Conference of 5th International Symposium on Ambient Intelligence, ISAmI 2014 ; Conference Date: 4 June 2014 Through 6 June 2014; Conference Code:116759},

keywords = {AIDA; Health information systems; Health organizations; Hospital service; INTCare; Medical information; Multi-agent platforms; Real time, Application programs; Artificial intelligence; Hospitals; Intelligent agents; Intensive care units; Interoperability; Medical information systems, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Miranda2011253,

title = {Evolutionary intelligence in agent modeling and interoperability},

author = {M. Miranda and J. Machado and A. Abelha and J. Neves and J. Neves},

editor = {Corchado J. M. Preuveneers D. Novais P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052975755&doi=10.1007%2f978-3-642-19937-0_33&partnerID=40&md5=278dc339bbfa3d2f6334669b614558df},

doi = {10.1007/978-3-642-19937-0_33},

issn = {18675662},

year  = {2011},

date = {2011-01-01},

journal = {Advances in Intelligent and Soft Computing},

volume = {92},

pages = {253-257},

abstract = {A healthcare organization to be tuned with the users expectations, and to act according to its goals, must be accountable for the quality, cost, and overall care of the beneficiaries. In this paper we describe a model of clinical information designed to make health information systems properly interoperable and safely computable, based on an Evolutionary Intelligence approach that generates quantified scenarios from defective knowledge. The model is a response to a number of requirements, ranging from the semantic ones to the evaluation of software performance at runtime; it is among the biggest challenges in engineering nowadays. © 2011 Springer-Verlag Berlin Heidelberg.},

note = {cited By 2},

keywords = {Agent modeling; Clinical information; Health information systems; Healthcare organizations; Runtimes; Software performance, Health care; Intelligent agents; Intelligent systems; Interoperability; Semantics; Software agents, Multi agent systems},

pubstate = {published},

tppubtype = {inproceedings}

}