rules extraction
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2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Rachid Flouchi ◽  
Abderrahim Elmniai ◽  
Mohamed El Far ◽  
Ibrahim Touzani ◽  
Naoufal El Hachlafi ◽  
...  

Background. The hospital environment, especially surfaces and medical devices, is a source of contamination for patients. Objective. This study carried out, to the best of our knowledge, for the first time at Taza Hospital in Morocco aimed to assess the microbiological quality of surfaces and medical devices in surgical departments and to evaluate the disinfection procedure in time and space. Methods. Samples were taken by swabbing after cleaning the hospital surface or medical device, to isolate and identify germs which were inoculated on semiselective culture media then identified by standard biochemical and physiological tests, using the analytical profile index (API) galleries. Moreover, the association rules extraction model between sites on the one hand and germs on the other hand was used for sampling. Results. The study showed that 83% of the samples have been contaminated after biocleaning. The most contaminated services have been men’s and women’s surgeries. 62% of isolated germs have been identified as Gram-positive bacteria, 29% as Gram-negative bacteria, and 9% as fungi. Concerning the association rules extraction model, a strong association between some contaminated sites and the presence of germ has been found, such as the association between wall and nightstand and door cuff, meaning that the wall and nightstand contamination is systematically linked to that of the door cuff. The disinfection procedure efficacy evaluation has enabled suggesting renewing it each 4 h. Conclusion. Microbiological monitoring of surfaces is necessary at hospital level through the use of the association rule extraction model, which is very important to optimize the sampling, cleaning, and disinfection site scenarios of the most contaminated ones.


2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Sima Hadadian ◽  
Zahra Naji Azimi ◽  
Nasser Motahari Farimani ◽  
Behrouz Minaei Bidgoli

2021 ◽  
pp. 179-184
Author(s):  
Massimo Aria ◽  
Corrado Cuccurullo ◽  
Agostino Gnasso

The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not allow users to understand how certain decisions are made. This drawback is identified with the term "Black-Box", as well as models that do not allow to interpret the internal work of certain ML techniques, thus discouraging their use. In a highly regulated and risk-averse context such as healthcare, although "trust" is not synonymous with decision and adoption, trusting an ML model is essential for its adoption. Many clinicians and health researchers feel uncomfortable with black box ML models, even if they achieve high degrees of diagnostic or prognostic accuracy. Therefore more and more research is being conducted on the functioning of these models. Our study focuses on the Random Forest (RF) model. It is one of the most performing and used methodologies in the context of ML approaches, in all fields of research from hard sciences to humanities. In the health context and in the evaluation of health policies, their use is limited by the impossibility of obtaining an interpretation of the causal links between predictors and response. This explains why we need to develop new techniques, tools, and approaches for reconstructing the causal relationships and interactions between predictors and response used in a RF model. Our research aims to perform a machine learning experiment on several medical datasets through a comparison between two methodologies, which are inTrees and NodeHarvest. They are the main approaches in the rules extraction framework. The contribution of our study is to identify, among the approaches to rule extraction, the best proposal for suggesting the appropriate choice to decision-makers in the health domain.


Author(s):  
Federico Antonello ◽  
Piero Baraldi ◽  
Ahmed Shokry ◽  
Enrico Zio ◽  
U. Gentile ◽  
...  

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096299
Author(s):  
Márcio Alencar ◽  
Raimundo Barreto ◽  
Horácio Fernandes ◽  
Eduardo Souto ◽  
Richard Pazzi

In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.


2020 ◽  
Vol 10 (19) ◽  
pp. 6804
Author(s):  
Àngela Nebot ◽  
Francisco Mugica ◽  
Félix Castro

In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. The introductory and didactic planning courses were analyzed using the proposed toolbox. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload.


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