HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

González–Vélez, Horacio; Mier, Mariola; Julià‐Sapé, Margarida; Arvanitis, Theodoros N.; García‐Gómez, Juan Miguel; Robles, Montserrat; Lewis, Paul; Dasmahapatra, Srinandan; Dupplaw, David; Peet, Andrew C.; Arús, Carles; Celda, Bernardo; Huffel, Sabine Van; Lluch-Ariet, Magí

doi:10.1007/s10489-007-0085-8

Cited by 86 publications

(64 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This assistant was considered to recover and use knowledge in order to solve medical episodes. They use case reasoning in order to capture the experiential knowledge of healthcare [5].  The HealthAgents system is the result of a funded research project [6].…”

Section: B Decision Support Systemsmentioning

confidence: 99%

A Multi Agent System for Hospital Organization

Jemal¹,

Kechaou²,

Ayed³

et al. 2015

IJMLC

View full text Add to dashboard Cite

Abstract-In some countries the domain of medical care is characterized by difficulty, dynamism and diversity. In the 21 st century healthcare represents different challenges (increasing cost of care, population growth and lack of caregivers). For that, cooperation in the Agent Technology can provide better healthcare than the traditional medical system. In fact, Intelligent Agents properties (sociability, proactivity, autonomy) and the features of Multi Agent Systems (management of distributed information, communication and cooperation between different entities) are a good option to solve several problems in the hospital organization. As examples of problems, which appear in the hospital, we cite: collaboration between hospital wards, elaborations of medical diagnostics, coordination among medical entities and the collection of information about patients etc. In this regard, we propose a new medical system called MMAS (Medical MultiAgent System). The proposed system is complex; it integrates Multi-Agent System in healthcare in order to make care as efficient as possible.Index Terms-Healthcare, agent technology, multi agent system, medical-multi-agent system.

show abstract

Section: B Decision Support Systemsmentioning

confidence: 99%

A Multi Agent System for Hospital Organization

Jemal¹,

Kechaou²,

Ayed³

et al. 2015

IJMLC

View full text Add to dashboard Cite

show abstract

“…Based on the knowledge acquired during the European projects eTumour [1] and HealthAgents [18] we defined several of the requirements and developed Curiam BT. Figure 2 shows the more fine-grained use case of Curiam BT.…”

Section: Curiam Btmentioning

confidence: 99%

Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as an additional information procedure for novice radiologists

Sáez¹,

Martí‐Bonmatí²,

Robles³

et al. 2014

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

ElsevierSáez Silvestre, C.; Martí-Bonmatí, L.; Alberich Bayarri, A.; Robles Viejo, M.; García Gómez, JM. (2014). Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as an additional information procedure for novice radiologists. Computers in Biology and Medicine. 45:26-33. doi:10.1016/j.compbiomed.2013.11 AbstractThe results of a randomized pilot study and qualitative evaluation of the clinical decision support system Curiam BT are reported. We evaluated the system's feasibility and potential value as a radiological information procedure complementary to magnetic resonance (MR) imaging to assist novice radiologists in diagnosing brain tumours using MR spectroscopy (1.5 and 3.0 Tesla). Fifty-five cases were analysed at three hospitals according to four non-exclusive diagnostic questions. Our results show that Curiam BT improved the diagnostic accuracy in all the four questions. Additionally, we discuss the findings of the users' feedback about the system, and the further work to optimize it for real environments and to conduct a large clinical trial.

show abstract

“…During the classification procedure the assigned class as well as the posterior probabilities of each class were displayed to the system [39,41] . HealthAgents HealthAgents [42] was a distributed DSS (d-DSS) built upon INTERPRET and eTUMOUR projects. The great difference of this project was its architectural structure since it was based on agent-based architecture in order to decentralize the process of brain tumor differentiation in a distributed decision support framework that supports data partitioning and sharing [43] .…”

Section: Voxel Assignment Accuracymentioning

confidence: 99%

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Tsolaki

Kousi

Svolos

et al. 2014

WJR

View full text Add to dashboard Cite

In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and noninvasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

show abstract

HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

Cited by 86 publications

References 41 publications

A Multi Agent System for Hospital Organization

A Multi Agent System for Hospital Organization

Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as an additional information procedure for novice radiologists

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Contact Info

Product

Resources

About