Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem

Dorado-Moreno, Manuel; Prez-Ortiz, Mara; Gutirrez, Pedro A.; Ciria, R.; Briceo, Javier; Hervs-Martnez, Csar

doi:10.1016/j.artmed.2017.02.004

Cited by 40 publications

(27 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Out of the 75 retrieved papers, 49 can be categorized as data-driven decision support approaches 10 – 58 . As regards to the addressed task, a large part of these contributions deals with prediction, intended as classification or regression 10 – 15 , 17 – 19 , 21 – 36 , 38 – 42 , 44 – 46 , 48 , 49 , 52 – 54 , 57 . One work is focused on association rule mining 20 , and one adopts statistics for risk analysis 55 .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Montani

Striani

2019

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

show abstract

Section: Resultsmentioning

confidence: 99%

“…NN deserve a special consideration, since they are the key technique adopted in many of the retrieved papers 15 , 17 – 19 , 28 – 30 , 35 , 40 , 41 , 52 , 58 . Indeed, image interpretation and classification are fields where NN/deep learning approaches work well 59 .…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Montani

Striani

2019

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…Another problem existing in the proposed method is the classification learning method for constructing basic classifiers (line 6 in Algorithm 1). In this paper, six conventional classifier learning methods, namely k-nearest neighbor (KNN) [40,41], C4.5 [42,43], logistic regression (LR) [44,45], support vector machine (SVM) [46,47], neural network (NN) [48][49][50] and naive Bayes (NB) [51,52], respectively, are selected as candidates to evaluate the performance of the proposed method.…”

Section: Majority Class D Majmentioning

confidence: 99%

Imbalanced Learning Based on Data-Partition and SMOTE

Guo

Zhou

2018

Information

View full text Add to dashboard Cite

Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method based on data-partition and SMOTE for imbalanced learning. The proposed method differs from conventional ones in both the learning and prediction stages. For the learning stage, the proposed method uses the following three steps to learn a class-imbalance oriented model: (1) partitioning the majority class into several clusters using data partition methods such as K-Means, (2) constructing a novel training set using SMOTE on each data set obtained by merging each cluster with the minority class, and (3) learning a classification model on each training set using convention classification learning methods including decision tree, SVM and neural network. Therefore, a classifier repository consisting of several classification models is constructed. With respect to the prediction stage, for a given example to be classified, the proposed method uses the partition model constructed in the learning stage to select a model from the classifier repository to predict the example. Comprehensive experiments on KEEL data sets show that the proposed method outperforms some other existing methods on evaluation measures of recall, g-mean, f-measure and AUC.

show abstract

“…Cruz-Ramírez et al used a multi-objective evolutionary algorithm to train RBFNNs to predict patient survival after liver transplantation [26]. Dorado-Moreno et al two approaches in combination, a cost-sensitive evolutionary ordinal ANN and an ordinal over-sampling technique, to tackle the same problem [27].…”

Section: Introductionmentioning

confidence: 99%

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

et al. 2020

Self Cite

View full text Add to dashboard Cite

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group.

show abstract

Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem

Cited by 40 publications

References 52 publications

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

Imbalanced Learning Based on Data-Partition and SMOTE

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Contact Info

Product

Resources

About