338,766 research outputs found
Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography
Background: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. Methods: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. Results: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P<0.001), presence of intraplaque hemorrhage (0.69, P<0.001), and plaque composition (0.78, P<0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1-205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7-69.4]; odds ratio, 95% CI). Conclusions: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making
Application of Artificial intelligence to high education: empowerment of flipped classroom with just-in-time teaching
[EN] In the so-called society 4.0, Artificial Intelligence (AI) is being widely used in
many areas of life. Machine learning uses mathematical algorithms based on
"training data", which are able to make predictions or take decisions with the
ability to change their behavior through a self-training approach.
Furthermore, thanks to AI, a large volume of data can be now processed with
the overall goal to extract patterns and transform the information into a
comprehensible structure for further utilization, which manually done by
humans would easily take several years. In this framework, this article
explores the potential of AI and machine learning to empower flipped
classroom with just-in-time teaching (JiTT). JiTT is a pedagogical method
that can be easily combined with the reverse teaching. It allows professors to
receive feedback from students before class, so they may be able to adapt the
lesson flow, as well as preparing strategies and activities focused on the
student deficiencies.
This research explores the application of AI in high education as a tool to
analyze the key variables involved in the learning process of students and to
integrate JiTT within the flipped classroom. Finally, a case of application of
this methodology is presented, applied to the course of Energy Markets
taught at the Polytechnic University of Valencia.This work was supported in part by the regional public administration of Valencia under the grant ACIF/2018/106.Montuori, L.; Alcázar Ortega, M.; Bastida Molina, P.; Vargas Salgado, CA. (2021). Application of Artificial intelligence to high education: empowerment of flipped classroom with just-in-time teaching. En Proceedings INNODOCT/20. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 223-231. https://doi.org/10.4995/INN2020.2020.11896OCS22323
On The Effect of Hyperedge Weights On Hypergraph Learning
Hypergraph is a powerful representation in several computer vision, machine
learning and pattern recognition problems. In the last decade, many researchers
have been keen to develop different hypergraph models. In contrast, no much
attention has been paid to the design of hyperedge weights. However, many
studies on pairwise graphs show that the choice of edge weight can
significantly influence the performances of such graph algorithms. We argue
that this also applies to hypegraphs. In this paper, we empirically discuss the
influence of hyperedge weight on hypegraph learning via proposing three novel
hyperedge weights from the perspectives of geometry, multivariate statistical
analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE,
Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to
graph learning, several representative hyperedge weighting schemes can be
concluded by our experimental studies. Moreover, the experiments also
demonstrate that the combinations of such weighting schemes and conventional
hypergraph models can get very promising classification and clustering
performances in comparison with some recent state-of-the-art algorithms
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
and CT images of 300 patients from four different cohorts were analyzed for the
risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
head-and-neck cancer. Prediction models combining radiomic and clinical
variables were constructed via random forests and imbalance-adjustment
strategies using two of the four cohorts. Independent validation of the
prediction and prognostic performance of the models was carried out on the
other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
potential of radiomics for assessing the risk of specific tumour outcomes using
multiple stratification groups. This could have important clinical impact,
notably by allowing for a better personalization of chemo-radiation treatments
for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7
figures, 8 table
On Pruning for Score-Based Bayesian Network Structure Learning
Many algorithms for score-based Bayesian network structure learning (BNSL),
in particular exact ones, take as input a collection of potentially optimal
parent sets for each variable in the data. Constructing such collections
naively is computationally intensive since the number of parent sets grows
exponentially with the number of variables. Thus, pruning techniques are not
only desirable but essential. While good pruning rules exist for the Bayesian
Information Criterion (BIC), current results for the Bayesian Dirichlet
equivalent uniform (BDeu) score reduce the search space very modestly,
hampering the use of the (often preferred) BDeu. We derive new non-trivial
theoretical upper bounds for the BDeu score that considerably improve on the
state-of-the-art. Since the new bounds are mathematically proven to be tighter
than previous ones and at little extra computational cost, they are a promising
addition to BNSL methods
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