Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain.

Back pain is the leading cause of disability worldwide. Its emergence relates not only to the musculoskeletal degeneration biological substrate but also to psychosocial factors; emotional components play a pivotal role. In modern society, people are significantly informed by the Internet; in turn, they contribute social validation to a "successful" digital information subset in a dynamic interplay. The Affective component of medical pages has not been previously investigated, a significant gap in knowledge since they represent a critical biopsychosocial feature. We tested the hypothesis that successful pages related to spine pathology embed a consistent emotional pattern, allowing discrimination from a control group. The pool of web pages related to spine or hip/knee pathology was automatically selected by relevance and popularity and submitted to automated sentiment analysis to generate emotional patterns. Machine Learning (ML) algorithms were trained to predict page original topics from patterns with binary classification. ML showed high discrimination accuracy; disgust emerged as a discriminating emotion. The findings suggest that the digital affective "successful content" (collective consciousness) integrates patients' biopsychosocial ecosystem, with potential implications for the emergence of chronic pain, and the endorsement of health-relevant specific behaviors. Awareness of such effects raises practical and ethical issues for health information providers.

Representing and querying now-relative relational medical data.

Temporal information plays a crucial role in medicine. Patients' clinical records are intrinsically temporal. Thus, in Medical Informatics there is an increasing need to store, support and query temporal data (particularly in relational databases), in order, for instance, to supplement decision-support systems. In this paper, we show that current approaches to relational data have remarkable limitations in the treatment of "now-relative" data (i.e., data holding true at the current time). This can severely compromise their applicability in general, and specifically in the medical context, where "now-relative" data are essential to assess the current status of the patients. We propose a theoretically grounded and application-independent relational approach to cope with now-relative data (which can be paired, e.g., with different decision support systems) overcoming such limitations. We propose a new temporal relational representation, which is the first relational model coping with the temporal indeterminacy intrinsic in now-relative data. We also propose new temporal algebraic operators to query them, supporting the distinction between possible and necessary time, and Allen's temporal relations between data. We exemplify the impact of our approach, and study the theoretical and computational properties of the new representation and algebra.

Temporal detection and analysis of guideline interactions.

BACKGROUND: Clinical practice guidelines (CPGs) are assuming a major role in the medical area, to grant the quality of medical assistance, supporting physicians with evidence-based information of interventions in the treatment of single pathologies. The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between CPGs. Several approaches have started to face such a challenging problem. However, they suffer from a substantial limitation: they do not take into account the temporal dimension. Indeed, practically speaking, interactions occur in time. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if the times of execution of such actions are such that their effects overlap in time. OBJECTIVES: We aim at devising a methodology to detect and analyse interactions between CPGs that considers the temporal dimension. METHODS: In this paper, we first extend our previous ontological model to deal with the fact that actions, goals, effects and interactions occur in time, and to model both qualitative and quantitative temporal constraints between them. Then, we identify different application scenarios, and, for each of them, we propose different types of facilities for user physicians, useful to support the temporal detection of interactions. RESULTS: We provide a modular approach in which different Artificial Intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to provide users with such facilities. We applied our methodology to two cases of comorbidities, using simplified versions of CPGs. CONCLUSION: We propose an innovative approach to the detection and analysis of interactions between CPGs considering different sources of temporal information (CPGs, ontological knowledge and execution logs), which is the first one in the literature that takes into account the temporal issues, and accounts for different application scenarios.

An artificial intelligence framework for compensating transgressions and its application to diet management.

Today, there is considerable interest in personal healthcare. The pervasiveness of technology allows to precisely track human behavior; however, when dealing with the development of an intelligent assistant exploiting data acquired through such technologies, a critical issue has to be taken into account; namely, that of supporting the user in the event of any transgression with respect to the optimal behavior. In this paper we present a reasoning framework based on Simple Temporal Problems that can be applied to a general class of problems, which we called cake&carrot problems, to support reasoning in presence of human transgression. The reasoning framework offers a number of facilities to ensure a smart management of possible "wrong behaviors" by a user to reach the goals defined by the problem. This paper describes the framework by means of the prototypical use case of diet domain. Indeed, following a healthy diet can be a difficult task for both practical and psychological reasons and dietary transgressions are hard to avoid. Therefore, the framework is tolerant to dietary transgressions and adapts the following meals to facilitate users in recovering from such transgressions. Finally, through a simulation involving a real hospital menu, we show that the framework can effectively achieve good results in a realistic scenario.

Managing proposals and evaluations of updates to medical knowledge: theory and applications.

The process of keeping up-to-date the medical knowledge stored in relational databases is of paramount importance. Since quality and reliability of medical knowledge are essential, in many cases physicians' proposals of updates must undergo experts' evaluation before possibly becoming effective. However, until now no theoretical framework has been provided in order to cope with this phenomenon in a principled and non-ad hoc way. Indeed, such a framework is important not only in the medical domain, but in all Wikipedia-like contexts in which evaluation of update proposals is required. In this paper we propose GPVM (General Proposal Vetting Model), a general model to cope with update proposal⧹evaluation in relational databases. GPVM extends the current theory of temporal relational databases and, in particular, BCDM - Bitemporal Conceptual Data Model - "consensus" model, providing a new data model, new operations to propose and accept⧹reject updates, and new algebraic operators to query proposals. The properties of GPVM are also studied. In particular, GPVM is a consistent extension of BCDM and it is reducible to it. These properties ensure consistency with most relational temporal database frameworks, facilitating implementation on top of current frameworks and interoperability with previous approaches.

Planning: supporting and optimizing clinical guidelines execution.

A crucial feature of computerized clinical guidelines (CGs) lies in the fact that they may be used not only as conventional documents (as if they were just free text) describing general procedures that users have to follow. In fact, thanks to a description of their actions and control flow in some semiformal representation language, CGs can also take advantage of Computer Science methods and Information Technology infrastructures and techniques, to become executable documents, in the sense that they may support clinical decision making and clinical procedures execution. In order to reach this goal, some advanced planning techniques, originally developed within the Artificial Intelligence (AI) community, may be (at least partially) resorted too, after a proper adaptation to the specific CG needs has been carried out.

Automatic checking of the correctness of clinical guidelines in GLARE.

Representing clinical guidelines is a very complex knowledge-representation task, requiring a lot of expertise and efforts. Nevertheless, guideline representations often contain several kinds of errors. Therefore, checking the well-formedness and correctness of a guideline representation is an important task, which can be drastically improved with the adoption of computer programs. In this paper, we discuss the advanced facilities provided by the GLARE system to assist physicians to produce correct representations of clinical guidelines.

Automatic treatment of temporal issues in clinical guidelines in the GLARE system.

Temporal constraints play a fundamental role in clinical guidelines. For example, temporal indeterminacy, constraints about duration, delays between actions and periodic repetitions of actions are essential in order to cope with clinical therapies. This paper proposes a computer-based approach in order to deal with temporal constraints in clinical guidelines. Specifically, it provides the possibility to represent such constraints and reason with them (i.e., perform inferences in the form of constraint propagation). We first propose a temporal representation formalism and two constraint propagation algorithms operating on it, and then we show how they can be exploited in order to provide clinical guideline systems with different temporal facilities. Our approach offers several advantages: for example, during the guideline acquisition phase, it enables to represent temporal constraints and to check their consistency; during the execution phase, it allows the physician to check the consistency between action execution-times and the constraints in the guidelines, and to provide query answering and temporal simulation facilities (e.g., when choosing among alternative paths in a guideline).

Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines.

OBJECTIVE: In this paper, we define a principled approach to represent temporal constraints in clinical guidelines and to reason (i.e., perform inferences in the form of constraint propagation) on them. We consider different types of constraints, including composite and repeated actions, and propose different types of temporal functionalities (e.g., temporal consistency checking). BACKGROUND: Constraints about actions, durations, delays and periodic repetitions of actions are an intrinsic part of most clinical guidelines. Although several approaches provide expressive temporal formalisms, only few of them deal with the related temporal reasoning issues. METHODOLOGY: We first propose a temporal representation formalism and two temporal reasoning algorithms. Then, we consider the trade-off between the expressiveness of the formalism and the computational complexity of the algorithms, in order to devise a correct, complete and tractable approach. Finally, we show how the algorithms can be exploited to provide clinical guideline systems with different types of temporal facilities. RESULTS: Our approach offers several advantages. During the guideline acquisition phase, it enables to represent temporal constraints, and to check their consistency. In the execution phase, it checks the consistency between the execution times of the actions and the constraints in the guidelines, and provides query answering and simulation facilities.

Advanced treatment of temporal phenomena in clinical guidelines.

Temporal constraints play a fundamental role in clinical guidelines. We sketch a computer-based temporal framework to represent temporal information in the guidelines, and to support different forms of inference and query-answering (which, e.g., might help in physician decision making).

Temporal consistency checking in clinical guidelines acquisition and execution: the GLARE's approach.

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the check of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. Such a treatment of temporal constraints involves the extension of various Artificial Intelligence techniques.