118,568 research outputs found
Dealing with temporal indeterminacy in relational databases: An AI methodology
No full textTime is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including AI and relational Temporal Databases (TDB). While temporally imprecise information has been widely studied by the AI community, only few approaches have faced temporal indeterminacy (in particular, “don’t know exactly when” indeterminacy) in TDBs. Indeed, as we will show in this paper, the treatment of time in general, and of temporal indeterminacy in particular, involves the introduction of implicit forms of data representation in TDBs. As a consequence, we propose a new AI -style methodology to cope with temporal indeterminacy in TDBs. Specifically, we show that typical AI notions and techniques, such as making explicit the semantics of the representation formalism, and adopting symbolic manipulation techniques based on such a semantics, can be fruitfully exploited in the development of a “principled ” treatment of indeterminate time in relational databases
Correction to: Lenvatinib as a salvage therapy for advanced metastatic medullary thyroid cancer (Journal of Endocrinological Investigation, (2021), 44, 10, (2139-2151), 10.1007/s40618-020-01491-3)
No full textThe article “Lenvatinib as a salvage therapy for advanced metastatic medullary thyroid cancer” written by A. Matrone, A. Prete, A. Nervo, A. Ragni, L. Agate, E. Molinaro, C. Giani, L. Valerio, E. Minaldi, A. Piovesan and R. Elise was originally published online on the publisher’s internet portal on 17th February 2021 with Open Access under a Creative Commons Attribution (CC BY) license 4.0 With the authors’ decision to cancel Open Access the copyright of the article changed on 28th April 2021 to © Italian Society of Endocrinology (SIE) 2021 with all rights reserved. The original article has been corrected
Supporting the distributed execution of clinical guidelines by multiple agents
No full textClinical guidelines (GLs) are widely adopted in order to improve the quality of patient care, and to optimize it. To achieve such goals, their application on a specific patient usually requires the interventions of different agents, with different roles (e.g., physician, nurse), abilities (e.g., specialist in the treatment of alcohol-related problems) and contexts (e.g., many chronic patients may be treated at home). Additionally, the responsibility of the application of a guideline to a patient is usually retained by a physician, but delegation of responsibility (of the whole guideline, or of a part of it) is often used
equired (e.g., delegation to a specialist), as well as the possibility, for a responsible, to select the executor of an action (e.g., a physician may retain the responsibility of an action, but delegate to a nurse its execution). To manage such phenomena, proper support to agent interaction and communication must be provided, providing agents with facilities for (1) treatment continuity (2) contextualization, (3) responsibility assignment and delegation (4) check of agent “appropriateness”. In this paper we extend GLARE, a computerized GL management system, to support such needs. We illustrate our approach by means of a practical case study
A constraint-based approach for the conciliation of clinical guidelines
No full textThe medical domain often arises new challenges to Artificial Intelligence. An emerging challenge is the support for the treatment of patients affected by multiple pathologies (comorbid patients). In the medical context, clinical practice guidelines (CPGs) are usually adopted to provide physicians with evidence-based recommendations, considering only single pathologies. To support physicians in the treatment of comorbid patients, suitable methodologies must be devised to “merge” CPGs. Techniques like replanning or scheduling, traditionally adopted in AI to “merge” plans, must be extended and adapted to fit the requirements of the medical domain. In this paper, we propose a novel methodology, that we term “conciliation”, to merge multiple CPGs, supporting the treatments of comorbid patients
META-GLARE’s Supports to Agent Coordination
No full textClinical Guidelines (GLs) provide evidence-based recommendations to suggest to physicians the “best” medical treatments, and are widely used to enhance the quality of patient care, and to optimize it. In many cases, the treatment of patients cannot be provided by a unique healthcare agent, operating in a unique context. For instance, the treatment of chronic patients is usually performed not only in the hospital, but also at home andor in the general practitioner’s ambulatory, and many healthcare agents (e.g., different specialist, nurses, family doctor) may be involved. To grant the quality of the treatments, all such agents must cooperate and interact. A computer-based support to GL execution is important to provide facilities for coordinating such different agents, and for granting that, at any time, the actions to be executed have a “proper” person in charge and executor, and are executed in the correct context. Additionally, also facilities to support the delegation of responsibility should also be considered. In this paper we extend META-GLARE, a computerized GL management system, to support such needs providing facilities for (1) treatment continuity (2) action contextualization, (3) responsibility assignment and delegation (4) check of agent “appropriateness”. Specific attention is also devoted to the temporal dimension, to grant that each action is executed according to the temporal constraints possibly present in the GL. We illustrate our approach by means of a practical case study
A dendrochronological analysis of Pinus pinea L. on the Italian mid-Tyrrhenian coast
No full textIn order to assess the response of the radial growth of Pinus pinea L. to climatic variability in Central Italy, dendrochronological and dendroclimatological analyses were carried out on five different populations scattered along the Tyrrhenian coasts of the peninsula. The aim of this study is to contribute to the understanding of the ecological demands of this species, particularly in the study area. For each site total ring, early-, and late-wood width chronologies were developed. Multidimensional analyses were performed for the three tree-ring datasets in order to analyze the relations between sites chronologies. Both Principal Component Analyses and hierarchical classifications highlighted an important difference of one site in respect to the other, probably due to site characteristics. Correlation functions were performed to infer the main climatic factors controlling the radial growth of the species. For a comparative study, we limited our attention to the common interval 1926-2003 (78 years) in which the response of the tree-ring chronologies to climate at both local and regional scale was investigated. Positive moisture balance in the late spring-summer period of the year of growth is the climatic driver of P. pinea radial growth in the study area. Moreover, this study shows how low summer temperatures strongly favor the radial growth of the species.Fil: Piraino, Sergio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mendoza. Instituto Argentino de Investigaciones de Zonas Aridas; ArgentinaFil: Camiz, Sergio. Universita degli studi di Roma "Sapienza". Dipartimento di Matematica; ItaliaFil: Di Filippo, Alfredo. Universita Degli Studi Della Tuscia. DendrologyLab; ItaliaFil: Piovesan, Gianluca. Universita Degli Studi Della Tuscia. DendrologyLab; ItaliaFil: Spada, Francesco. Universita degli studi di Roma "Sapienza". Dipartimento de biología ambientale; Itali
Temporal detection and analysis of guideline interactions
No full textBackground
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
Supporting Physicians in the Detection of the Interactions between Treatments of Co-Morbid Patients
No full textThe treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for modern healthcare. Clinical practice guidelines are widely used to support physicians, providing them evidence-based information of interventions, but only on individual pathologies. This sets up the urgent need of developing methodologies to support physicians in the detection of interactions between guidelines, to help them in the treatment of comorbid patients. In this chapter, the authors identify different levels of abstractions in the analysis of interactions, based on both the hierarchical organization of clinical guidelines (in which composite actions are refined into their components) and the hierarchy of drug categories. They then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over multiple levels of abstraction.No Full Tex
A 1NF temporal relational model and algebra coping with valid-time temporal indeterminacy
No full textIn the real world, many phenomena are time related and in the last three decades the database community has devoted much work in dealing with “time of facts” in databases. While many approaches incorporating time in the relational model have been already devised, most of them assume that the exact time of facts is known. However, this assumption does not hold in many practical domains, in which temporal indeterminacy of facts occurs. The treatment of valid-time indeterminacy requires in-depth extensions to the current relational approaches. In this paper, we propose a theoretically grounded approach to cope with this issue, overcoming the limitations of related approaches in the literature. In particular, we present a 1NF temporal relational model and propose a new temporal relational algebra to query it. We also formally study the properties of the new data model and algebra, thus granting that our approach is interoperable with pre-existent temporal and non-temporal relational approaches, and is implementable on top of them. Finally, we consider computational complexity, showing that only a limited overhead is added when moving from determinate to indeterminate time.Full Tex
Reasoning and querying bounds on differences with layered preferences
No full textArtificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and
calories. Recently, some approaches have extended the
BoDs framework in a fuzzy, “noncrisp” direction,
considering probabilities or preferences. While previous
approaches have mainly aimed at providing an
optimal solution to the set of constraints, we propose
an innovative class of approaches in which constraint
propagation algorithms aim at identifying the “space of
solutions” (i.e., the minimal network) with their preferences,
and query answering mechanisms are provided
to explore the space of solutions as required, for
example, in decision support tasks. Aiming at generality,
we propose a class of approaches parametrized
over user‐defined scales of qualitative preferences (e.g.,
Low, Medium, High, and Very High), utilizing the resume
and extension operations to combine preferences,
and considering different formalisms to associate preferences
with BoDs. We consider both “general” preferences
and a form of layered preferences that we call
“pyramid” preferences. The properties of the class of
approaches are also analyzed. In particular, we show
that, when the resume and extension operations are
defined such that they constitute a closed semiring, a
more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation
of the constraint propagation algorithms
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