Checking the quality of clinical guidelines using automated reasoning tools

Hommersom, Arjen; Lucas, Peter; Bommel, P. van

doi:10.1017/s1471068408003451

Cited by 11 publications

(5 citation statements)

References 52 publications

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“…One of such issues is the verification of properties of clinical guidelines, i.e., in order to improve quality of guidelines, proving that some properties, expressed in a temporal logic, hold for all executions of a guideline, whose model is expressed in a formal language. Theorem proving techniques have been adopted for verification in the Protocure and Protocure II projects [22], [25], while model checking techniques have been explored in Protocure II and GLARE [23], [24].…”

Section: Conclusion and Related Workmentioning

confidence: 99%

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Spiotta

Terenziani

Dupré

2017

IEEE Trans. Knowl. Data Eng.

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Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper we explore the interplay between CGs and BMK from the viewpoint of a-posteriori conformance analysis, intended as the adherence of a specific execution log to both the CG and the BMK. In this paper we consider also the temporal dimension: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in Answer Set Programming with respect to a guideline and BMK, pointing out discrepanciesincluding temporal discrepancies -with respect to the different knowledge sources, and providing explanations regarding how the applications of the CG and the BMK have interacted, especially in case strictly adhering to both is not possible.

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Section: Conclusion and Related Workmentioning

confidence: 99%

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Spiotta

Terenziani

Dupré

2017

IEEE Trans. Knowl. Data Eng.

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“…Due to the growing need to offer user support in decisionmaking processes some studies have been presented [71,72], related to the qualitative models and qualitative reasoning in Database Theory and in AI research. With respect to the problem of knowledge representation and reasoning mechanisms in LP, a measure of the quality-of-information (QoI) of such programs has been object of some work with promising results [73][74][75]. The QoI [17] with respect to the extension of a predicate i will be given by a truth-value in the interval [0,1], i.e., if the information is known (positive) or false (negative) the QoI for the extension of predicate i is 1.…”

Section: Quality-of-informationmentioning

confidence: 99%

Evolutionary intelligence in asphalt pavement modeling and quality-of-information

et al. 2012

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The analysis and development of a novel approach to asphalt pavement modeling, able to attend the need to predict the failure according to technical and non-technical criteria in a highway, is a hard task, namely in terms of the huge amount of possible scenarios. Indeed, the current state-of-the-art for service-life prediction is at empiric and empiric-mechanistic levels, and does not provide any suitable answer even for a single failure criteria. Consequently, it is imperative to achieve qualified models and qualitative reasoning methods, in particular due to the need to have firstclass environments at our disposal where defective information is at hand. To fulfill this goal, this paper presents a dynamic and formal model oriented to fulfill the task of making predictions for multi-failure criteria, in particular

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“…With all this complexity, a large number of errors can occur (Byleveld et al 2008). A failure can be defined as failure of a planned action and use of an incorrect plan, and these can be in connection with products, processes, and systems (Hommerson et al 2008). The best way to prevent similar errors from happening again is to report them, i.e., create experiential learning systems to identify their causes.…”

Section: Introductionmentioning

confidence: 99%

Avoidance of operational sampling errors in drinking water analysis

Fernandes

Figueiredo

Ribeiro

et al. 2022

Journal of Water Supply: Research and Technology-Aqua

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The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.

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Checking the quality of clinical guidelines using automated reasoning tools

Cited by 11 publications

References 52 publications

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Evolutionary intelligence in asphalt pavement modeling and quality-of-information

Avoidance of operational sampling errors in drinking water analysis

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