META-GLARE: A meta-system for defining your own computer interpretable guideline system—Architecture and acquisition

Bottrighi, Alessio; Terenziani, Paolo

doi:10.1016/j.artmed.2016.07.002

Cited by 19 publications

(44 citation statements)

References 21 publications

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“…, … , -∈ " denote the s lowest qualitative values in the scale " (i.e., # = " ( ), 1 ≤ ≤ ). ■ For instance, the constraint between two consecutive administrations of NA (NA1 and NA2) in Example 2 can be represented through the "compact" PyP_STP constraint ⟨ 1, 2, ⟨⟨ [10,15], ⟩, ⟨ [11,13], ⟩, ⟨ [12,12], ℎ ℎ⟩⟩⟩.…”

Section: Definition 2 Pyramid Preference Function (Ppf) a Pyramid Pmentioning

confidence: 99%

“…For example, the "compact" PyP_STP ⟨ 1, 2, ⟨⟨ [10,15], ⟩, ⟨ [11,13], ⟩, ⟨ [12,12], ℎ ℎ⟩⟩⟩ represents the fact that the difference between NA2 and NA1 has preference "high" if it is exactly 12, "medium" if it is 11 or 13, and "low" if it is 10, or between 14 and 15.…”

Section: Definition 2 Pyramid Preference Function (Ppf) a Pyramid Pmentioning

confidence: 99%

“…For instance, the resume operation applied to the two following "compact" PyP_STP constraints, concerning RT and NA1 and concerning NA1 and NA2 respectively, produces as a result a new PyP_STP constraint concerning RT and NA2 ƒ , 1, …⟨ [5,24], ⟩, ⟨ [6,24], ⟩, ⟨ [6,24], ℎ ℎ⟩ ‡ˆ⊙ / ⟨ 1, 2, ⟨⟨ [10,15], ⟩, ⟨ [11,13], ⟩, ⟨ [12,12], ℎ ℎ⟩⟩⟩⟩ = ⟨ , 2, …⟨ [15,39], ⟩, ⟨ [17,37], ⟩, ⟨ [18,36], ℎ ℎ⟩ ‡⟩ Complexity of the ⊕ and ⊙ operators. Since the intersection and the sum of two intervals can be computed in constant time and they are computed for each preference value, both the ⊕ 7 and ⊙ 7 operators can operate in time Θ( ), where r is the number of preference values in Sr, thus ⊕ = ⊙ = Θ( ).…”

Section: Definition Extension (⊙ Pmentioning

confidence: 99%

“…As an example of application of our version of the Compute-Summaries algorithm, in Figure 1 we report with dashed edges labelled with red lettering some of the "compact" PyP_STP constraints obtained from the application of the algorithm to Example 2 (note that, due to graphic reasons, we cannot represent all the PyP_STP constraints of the minimal network). For example, the Compute-Summaries algorithm adopting our ⊕ P and ⊙ P operators has restricted the original constraint between RT and NA1 to the constraint ⟨ , 1, ⟨⟨ [6,14], ⟩, ⟨ [9,13], ⟩, ⟨ [11,12], ℎ ℎ⟩⟩⟩, which limits the first administration of NA between 6am and 2pm with a "low" preference, between 9am and 1pm with a "medium" preference, and between 11am and 12pm…”

Section: Definition Extension (⊙ Pmentioning

confidence: 99%

“…Although our approach is domain-and task-independent, we aim at applying it mainly within our GLARE project [12], to support physicians in the treatment of comorbid patients [13], by integrating medical preferences in the reasoning process. Moreover, we also wish to investigate its integration in temporal relational databases for dealing with temporal indeterminacy [14][15][16][17][18].…”

Section: Comparisons and Conclusionmentioning

confidence: 99%

See 4 more Smart Citations

Temporal Reasoning with Layered Preferences

Anselma

Mazzei

Piovesan

et al. 2018

Lecture Notes in Computer Science

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Temporal representation and temporal reasoning is a central in Artificial Intelligence. The literature is moving to the treatment of "non-crisp" temporal constraints, in which also preferences or probabilities are considered. However, most approaches only support numeric preferences, while, in many domain applications, users naturally operate on "layered" scales of values (e.g., Low, Medium, High), which are domain-and task-dependent. For many tasks, including decision support, the evaluation of the minimal network of the constraints (i.e., the tightest constraints) is of primary importance. We propose the first approach in the literature coping with layered preferences on quantitative temporal constraints. We extend the widely used simple temporal problem (STP) framework to consider layered user-defined preferences, proposing (i) a formal representation of quantitative constraints with layered preferences, and (ii) a temporal reasoning algorithm, based on the general algorithm Compute-Summaries, for the propagation of such temporal constraints. We also prove that our temporal reasoning algorithm evaluates the minimal network.

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Section: Definition 2 Pyramid Preference Function (Ppf) a Pyramid Pmentioning

confidence: 99%

Section: Definition 2 Pyramid Preference Function (Ppf) a Pyramid Pmentioning

confidence: 99%

Section: Definition Extension (⊙ Pmentioning

confidence: 99%

Section: Definition Extension (⊙ Pmentioning

confidence: 99%

Section: Comparisons and Conclusionmentioning

confidence: 99%

See 3 more Smart Citations

Temporal Reasoning with Layered Preferences

Anselma

Mazzei

Piovesan

et al. 2018

Lecture Notes in Computer Science

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Reasoning and querying bounds on differences with layered preferences

et al. 2021

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Bounds on differences are widely used in AI to model binary constraints regarding different dimensions, such as time, space, costs, calories, etc. Representing and reasoning with them is an important task in several areas such as knowledge representation, scheduling and planning. Researchers are increasingly focusing on the treatment of fuzzy or probabilistic constraints, to deal with preferences and/or uncertainty. Current approaches to constraints with preferences focus on the evaluation of the optimal (i.e., with highest preference) solutions for the set of constraints and propose a wide range of alternative operators to combine preferences within constraint propagation. However, in decision support tasks, finding a specific (though optimal) solution is not the main goal, but rather it is more important to identify the "space of solutions" (i.e., the minimal network) with their preferences, and to provide users with query answering mechanisms to explore it. We propose the first approach that addresses such a need by (i) supporting user-defined layered scales of preferences (e.g., Low, Medium, High, Very High), (ii) proposing a family of extensions of bounds on differences constraints to deal with such layered preferences, (iii) defining a family of reasoning algorithms to evaluate the minimal network, which is parametric with respect to the basic operations to combine preferences (and the scale of preferences), and (iv) providing suitable query-answering facilities. The properties of the family of approaches are also analyzed.

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Querying Probabilistic Temporal Constraints for Guideline Interaction Analysis: GLARE’s Approach

Andolina¹,

Anselma

Piovesan

et al. 2018

Advances in Artificial Intelligence - IBERAMIA 2018

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The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. However, practically speaking, such interactions occur over time. The GLARE project explicitly provides knowledge representation, temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, the effects of actions have a probabilistic distribution in time, and being able to reason (through constraint propagation) and to query probabilistic temporal constraints further enhances the support for interaction detection.

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META-GLARE: A meta-system for defining your own computer interpretable guideline system—Architecture and acquisition

Cited by 19 publications

References 21 publications

Temporal Reasoning with Layered Preferences

Temporal Reasoning with Layered Preferences

Reasoning and querying bounds on differences with layered preferences

Querying Probabilistic Temporal Constraints for Guideline Interaction Analysis: GLARE’s Approach

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