A new decision support model for preanesthetic evaluation

Sobrie, Olivier; Lazouni, Mohammed El Amine; Mahmoudi, Saïd; Mousseau, Vincent; Pirlot, Marc

doi:10.1016/j.cmpb.2016.05.021

Cited by 22 publications

(20 citation statements)

References 33 publications

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“…In this model, which may also accommodate nonveto conditions, the criteria weights are substituted by a capacity , which allows criteria interactions (positive/negative synergies) to be modeled. These two types of extensions are the subject of ongoing investigations (see Sobrie et al, ; Meyer and Olteanu, ).…”

Section: Resultsmentioning

confidence: 99%

“…The interested reader will find in Sobrie et al. () a medical application of the MR‐Sort method in the domain of preanesthesia patient assessment; Sobrie et al. () also discuss the issue of interpretability of MR‐Sort models.…”

Section: Methodsmentioning

confidence: 99%

“…On the contrary, an applicant characterized by the performance vector (0.222, 0.889, 0.5, 0.833) is considered "not suitable" for the job since it lies under the profile level on criteria 1 and 3 (0.222 < 0.556 on criterion 1 and 0.5 < 0.667 on criterion 3). The interested reader will find in Sobrie et al (2016) a medical application of the MR-Sort method in the domain of preanesthesia patient assessment; Sobrie et al (2016) also discuss the issue of interpretability of MR-Sort models.…”

Section: Model Interpretationmentioning

confidence: 99%

“…26 (2019) (positive/negative synergies) to be modeled. These two types of extensions are the subject of ongoing investigations (see Sobrie et al, 2016;Meyer and Olteanu, 2017).…”

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confidence: 99%

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Learning monotone preferences using a majority rule sorting model

Sobrie

Mousseau

Pirlot

2018

Int Trans Operational Res

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We consider the problem of learning a function assigning objects into ordered categories. The objects are described by a vector of attribute values and the assignment function is monotone w.r.t. the attribute values (monotone sorting problem). Our approach is based on a model used in multicriteria decision analysis (MCDA), called MR‐Sort. This model determines the assigned class on the basis of a majority rule and an artificial object that is a typical lower profile of the category. MR‐Sort is a simplified variant of the ELECTRE TRI method. We describe an algorithm designed for learning such a model on the basis of assignment examples. We compare its performance with choquistic regression, a method recently proposed in the preference learning community, and with UTADIS, another MCDA method leaning on an additive value function (utility) model. Our experimentation shows that MR‐Sort competes with the other two methods, and leads to a model that is interpretable.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Model Interpretationmentioning

confidence: 99%

“…26 (2019) (positive/negative synergies) to be modeled. These two types of extensions are the subject of ongoing investigations (see Sobrie et al, 2016;Meyer and Olteanu, 2017).…”

mentioning

confidence: 99%

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Learning monotone preferences using a majority rule sorting model

Sobrie

Mousseau

Pirlot

2018

Int Trans Operational Res

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“…In this cohort, there was agreement in 75% and 58% of the ASA 1 and 2 patients respectively, with 25% of the ASA 1 group and 17% of the ASA 2 group subsequently given a higher classification. Computer models are now being developed that are capable of allocating a patient's ASA classification with over 90% accuracy . However, until such programs are routinely used in clinical practice, there will continue to be a wide variation in ASA assignments by individual anaesthetists, with the knock‐on effect of disagreement regarding what pre‐operative investigations are necessary.…”

Section: Categorisationmentioning

confidence: 99%

Pre-operative testing guidelines: a NICE try but not enough

Dhatariya

Wiles

2016

Anaesthesia

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A Population-Based Algorithm for Learning a Majority Rule Sorting Model with Coalitional Veto

Sobrie

Mousseau

Pirlot

2017

Lecture Notes in Computer Science

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MR-Sort (Majority Rule Sorting) is a multiple criteria sorting method which assigns an alternative a to category C h when a is better than the lower limit of C h on a weighted majority of criteria, and this is not true with the upper limit of C h. We enrich the descriptive ability of MR-Sort by the addition of coalitional vetoes which operate in a symmetric way as compared to the MR-Sort rule w.r.t. to category limits, using specific veto profiles and veto weights. We describe a heuristic algorithm to learn such an MR-Sort model enriched with coalitional veto from a set of assignment examples, and show how it performs on real datasets. 2 Considering Vetoes in MR-Sort 2.1 MR-Sort Model MR-Sort is a method for assigning objects to ordered categories. It is a simplified version of ELECTRE TRI, another MCDA method [10, 11]. The MR-Sort rule works as follows. Formally, let X be a set of objects evaluated on n ordered attributes (or criteria), F = {1, ..., n}. We assume that X is the Cartesian product of the criteria scales, X = n j=1 X j , each scale X j being completely ordered by the relation ≥ j. An object a ∈ X is a vector (a 1 ,. .. , a j ,. .. , a n), where a j ∈ X j for all j. The ordered categories which

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A new decision support model for preanesthetic evaluation

Cited by 22 publications

References 33 publications

Learning monotone preferences using a majority rule sorting model

Learning monotone preferences using a majority rule sorting model

Pre-operative testing guidelines: a NICE try but not enough

A Population-Based Algorithm for Learning a Majority Rule Sorting Model with Coalitional Veto

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