Using Description Logic and Abox Abduction to Capture Medical Diagnosis

Obeid, Mariam; Obeid, Zeinab; Moubaiddin, Asma; Obeid, Nadim

doi:10.1007/978-3-030-22999-3_33

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…The kinetics behind the oxidation of ethylene diaminetetraacetic acid (EDTA) by bromamine-T (BAT) was studied by Obeid in a pH 5 buffer. 91 This study suggested that the rate of the reaction showed a first-order dependence on [BAT] and a fractional-order dependence on [EDTA] and [H + ]. Neither the addition of p -toluenesulfonamide nor variation of the ionic strength of the medium had any impact on the reaction kinetics.…”

Section: Organic Transformations Using Bromamine-tmentioning

confidence: 89%

Bromamine-T (TsNBrNa): A Biologically Significant, Versatile Bromo-Organic Reagent

Kashyap¹,

Phukan²

2023

Synlett

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Bromamine-T (BAT) is a distinguished member of a special class of compounds called organic haloamines, possessing oxidising properties. It can act as the source of halonium ions, hypohalite species, and N-anions, along with the display of basic as well as nucleophilic properties. Bromamine-T finds wide application as an efficient reagent for various organic transformations such as aminobromination, amination, oxidation etc., under mild conditions with minimum toxicity. Moreover, due to its immense biological significance, this reagent is piquing the interest of numerous researchers.

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Section: Organic Transformations Using Bromamine-tmentioning

confidence: 89%

Bromamine-T (TsNBrNa): A Biologically Significant, Versatile Bromo-Organic Reagent

Kashyap¹,

Phukan²

2023

Synlett

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“…Model-based systems, which are typically considered more transparent, are also in need of explanation mechanisms. For instance, Vassiliades, Bassiliades, and Patkos (2021) surveys the important methods that use argumentation (Modgil et al, 2013) to provide explainability in AI, with for example applications in medical diagnosis (Obeid et al, 2019). Abstract argumentation frameworks introduce an abstract formalism to explain argumentative acceptance ( Šešelja & Straßer, 2013;Liao & Van Der Torre, 2020;Ulbricht & Wallner, 2021).…”

Section: Related Workmentioning

confidence: 99%

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

Gamba,

Bogaerts,

Guns

2023

jair

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We build on a recently proposed method for stepwise explaining the solutions to Constraint Satisfaction Problems (CSPs) in a human understandable way. An explanation here is a sequence of simple inference steps where simplicity is quantified by a cost function. Explanation generation algorithms rely on extracting Minimal Unsatisfiable Subsets (MUSs) of a derived unsatisfiable formula, exploiting a one-to-one correspondence between so-called non-redundant explanations and MUSs. However, MUS extraction algorithms do not guarantee subset minimality or optimality with respect to a given cost function. Therefore, we build on these formal foundations and address the main points of improvement, namely how to generate explanations efficiently that are provably optimal (with respect to the given cost metric). To this end, we developed (1) a hitting set-based algorithm for finding the optimal constrained unsatisfiable subsets; (2) a method for reusing relevant information across multiple algorithm calls; and (3) methods for exploiting domain-specific information to speed up the generation of explanation sequences. We have experimentally validated our algorithms on a large number of CSP problems. We found that our algorithms outperform the MUS approach in terms of explanation quality and computational time (on average up to 56 % faster than a standard MUS approach).

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“…While the main focus of XAI research has been on explaining black-box machine learning systems (Lundberg & Lee, 2017;Guidotti, Monreale, Ruggieri, Turini, Giannotti, & Pedreschi, 2018;Ignatiev, Narodytska, & Marques-Silva, 2019), also model-based systems, which are typically considered more transparent, are in need of explanation mechanisms. For instance, Vassiliades, Bassiliades, and Patkos (2021) survey the important methods that use argumentation (Modgil, Toni, Bex, Bratko, Chesnevar, Dvořák, Falappa, Fan, Gaggl, García, et al, 2013) to provide explainability in AI, with for example applications in medical diagnosis (Obeid, Obeid, Moubaiddin, & Obeid, 2019). Abstract argumentation frameworks introduce an abstract formalism to explain argumentative acceptance ( Šešelja & Straßer, 2013;Liao & Van Der Torre, 2020;Ulbricht & Wallner, 2021).…”

Section: Related Workmentioning

confidence: 99%

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

Gamba

Bogaerts

Guns

2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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We build on a recently proposed method for explaining solutions of constraint satisfaction problems. An explanation here is a sequence of simple inference steps, where the simplicity of an inference step is measured by the number and types of constraints and facts used, and where the sequence explains all logical consequences of the problem. We build on these formal foundations and tackle two emerging questions, namely how to generate explanations that are provably optimal (with respect to the given cost metric) and how to generate them efficiently. To answer these questions, we develop 1) an implicit hitting set algorithm for finding optimal unsatisfiable subsets; 2) a method to reduce multiple calls for (optimal) unsatisfiable subsets to a single call that takes constraints on the subset into account, and 3) a method for re-using relevant information over multiple calls to these algorithms. The method is also applicable to other problems that require finding cost-optimal unsatiable subsets. We specifically show that this approach can be used to effectively find sequences of optimal explanation steps for constraint satisfaction problems like logic grid puzzles.

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Using Description Logic and Abox Abduction to Capture Medical Diagnosis

Cited by 6 publications

References 23 publications

Bromamine-T (TsNBrNa): A Biologically Significant, Versatile Bromo-Organic Reagent

Bromamine-T (TsNBrNa): A Biologically Significant, Versatile Bromo-Organic Reagent

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

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