The automatic interpretation of clinical recommendations is a difficult task, even more so when it involves the processing of complex temporal constraints. In order to address this issue, a web-based system is presented herein. Its underlying model provides a comprehensive representation of temporal constraints in Clinical Practice Guidelines. The expressiveness and range of the model are shown through a case study featuring a Clinical Practice Guideline for the diagnosis and management of colon cancer. The proposed model was sufficient to represent the temporal constraints in the guideline, especially those that defined periodic events and placed temporal constraints on the assessment of patient states. The web-based tool acts as a health care assistant to health care professionals, combining the roles of focusing attention and providing patient-specific advice.

Clinical Practice Guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial Intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of Computer-Interpretable Guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial Intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are Expert Systems, Case-Based Reasoning, Medical Ontologies and Reasoning under Uncertainty, with a special focus on methodologies for assessing Quality of Information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a 1 Corresponding author 2 guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for Clinical Practice Guidelines and a decision model for a guideline-based Expert System that manages non-compliance with clinical guidelines and uncertainty

A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.

The main purpose to attain with the advent of clinical decision support systems is either to improve the quality of patient care or to reduce the occurrence of clinical malpractice, such as medical errors and defensive medicine. It is therefore necessary a machine-readable support to integrate the recommendations of Clinical Practice Guidelines in such systems. CompGuide is a Computer-Interpretable Guideline model developed under Ontology Web Language that offers support for administrative information concerning a guideline, workflow procedures, and the definition of clinical and temporal constraints. When compared to other models of the same type, besides having a comprehensive task network model, it introduces new temporal representations and the possibility of reusing pre-existing knowledge and integrating it in a guideline.

Negotiation is a collaborative activity that requires the participation of different parties whose behaviours influence the outcome of the whole process. The work presented here focuses on the identification of such behaviours and their impact on the negotiation process. The premise for this study is that identifying and cataloguing the behaviour of parties during a negotiation may help to clarify the role stress plays in the process. To do so, an experiment based on a negotiation game was implemented. During this experiment, behavioural and contextual information about participants was acquired. The data from this negotiation game was analysed in order to identify the conflict styles used by each party and to extract behavioural patterns from the interactions, useful for the development of plans and suggestions for the associated participants. In sooth, the work highlights the importance of the knowledge about social interactions as a basis for informed decision support in situations of conflict.