Fuzzy-ART neural networks for triage in pleural tuberculosis

Orjuela-Cañón, Álvaro David; Seixas, JM

doi:10.1109/pahce.2013.6568342

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…It is possible to see that the results in pre-diagnostic applications offer a difference of 1.05 % for the two approaches, showing that just pre-test information can be useful. This could be explaining by the use of variables most important in the detection task, characteristics that have been studied in previously works [9] [13],where the anamnesis variables are enough to detect pTB with results close to 90% in sensitivity.…”

Section: Discussionmentioning

confidence: 95%

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SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

Orjuela-Cañón¹,

Seixas²,

Trajman³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Diagnostic tasks in a contagious disease as pleural tuberculosis is essential to send the patients to a correct treatment to cure the ailment. Tools for supporting this task are still a challenge because different variables are necessary, including invasive standard tests. In the present work Self Organizing Maps were used for clustering of patients of pleural tuberculosis in three risk groups. The first approach employs just anamnesis variables and a second methodology uses additional information about some classical result tests. The last approach exhibits best results compared to the approach using anamneses variables

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Section: Discussionmentioning

confidence: 95%

“…Also, for pTB clustering is an alternative to classify the patients in low, medium and high risk of having the disease. Fuzzy-ART neural networks were implemented to do this task, reaching good classification results [13].…”

Section: Introductionmentioning

confidence: 99%

SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

Orjuela-Cañón¹,

Seixas²,

Trajman³

2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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“…Results achieved: sensitivity 81,3%, FP has decreased from 4,5 to 1,5 per image. Orjuela, Canon [2] demonstrate the results of experiments with Fuzzy-ARTNN classifier and neural network based on adaptive resonance theory with application of fuzzy logic where the results of sensitivity equal to 93,75% -96,87% were achieved.…”

Section: Classificationmentioning

confidence: 89%

Advanced approaches to computer-aided detection of thoracic diseases on chest X-rays

Zakirov¹,

Kuleev²,

Vladimirov³

2015

ams

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The research focus of the current paper are modern algorithms for solving the problem of automatic diagnostic of thorax diseases based on X-ray images. Special attention was paid to image preprocessing, classification together with calculation of features and their comparison in terms of efficiency. Approaches mentioned in this paper are used for development of new algorithm for automatic diagnostic of medical images.

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“…In the case of TB, [25] and [26] show how an artificial neural network (ANN) can be trained to diagnose TB using clinical data (CD), and in [26] and [27] variants of ANN for clustering are used to determine three risk groups (high, medium, and low risk) of the population concerning TB, showing good results.…”

Section: Of 16mentioning

confidence: 99%

Data Fusion Using Medical Records and Clinical Data to Support TB Diagnosis

Romero-Gómez,

Orjuela-Cañón,

Jutinico

et al. 2024

Preprint

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Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.

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Fuzzy-ART neural networks for triage in pleural tuberculosis

Cited by 7 publications

References 2 publications

SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic

Advanced approaches to computer-aided detection of thoracic diseases on chest X-rays

Data Fusion Using Medical Records and Clinical Data to Support TB Diagnosis

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