Autonomous agents and multi-agent systems applied in healthcare

Linked Argumentation Graphs for Multidisciplinary Decision Support

Multidisciplinary clinical decision-making has become increasingly important for complex diseases, such as cancers, as medicine has become very specialized. Multiagent systems (MASs) provide a suitable framework to support multidisciplinary decisions. In the past years, a number of agent-oriented approaches have been developed on the basis of argumentation models. However, very limited work has focused, thus far, on systematic support for argumentation in communication among multiple agents spanning various decision sites and holding varying beliefs. There is a need for an appropriate argumentation scheme and identification of recurring styles or patterns of multiagent argument linking to enable versatile multidisciplinary decision applications. We propose, in this paper, a method of linked argumentation graphs and three types of patterns corresponding to scenarios of agents changing the minds of others (argumentation) and their own (belief revision): the collaboration pattern, the negotiation pattern, and the persuasion pattern. This approach is demonstrated using a case study of breast cancer and lifelong recommendations, as the survival rates of diagnosed cancer patients are rising and comorbidity is the norm.

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain

Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditions demand so, and harness the overwhelming amount of patient related data currently at clinicians' disposal. However, these two approaches are rarely synergistically exploited, and tend to be used without integration. In this paper, we follow recent efforts reported in the literature regarding integration of BDI agency with machine learning based Cognitive Services, by proposing an integration architecture, and by validating such architecture in the complex domain of trauma management. In particular, we show that augmentation of a BDI agent, endowed with predefined plans encoding experts' knowledge, with a Cognitive Service, trained on past observed data, can enhance trauma management by reducing over triage episodes.

A Simple Framework of Smart Geriatric Nursing considering Health Big Data and User Profile

The National Bureau of Statistics of China shows that the population over 65 years old in China exceeds 166 million accounting for 11.93% of the total population by the end of 2018. The importance and severity of taking care of the elderly are becoming increasingly prominent. High-quality and meticulous care for the daily life of the elderly needs helpful and advanced sciences and technologies. Smart geriatric nursing is a must. Basing on the professional knowledge of geriatric nursing, this paper proposes a framework of smart geriatric nursing which consists of three aspects of smart nursing: smart geriatric nursing in physical health using biosensor and advanced devices, smart geriatric nursing in mental health based on user profile, and smart geriatric nursing for daily life based on big data in health. The deployment of the proposed method relies on the technologies of the Internet of Things (IoT), user profile system, big data, and many other advanced information technologies. The framework of methods can provide a useful reference for the systematic technical scheme of smart geriatric nursing in an aging society.

Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare

Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional what-if scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management.