Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease

Badnjević, Almir; Cifrek, Mario; Koruga, Dragan; Osmanković, Dinko

doi:10.1186/1472-6947-15-s3-s1

Cited by 62 publications

(30 citation statements)

References 35 publications

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“…In designing neural networks for solving specific problem, factors such as neural network architecture, number of hidden neurons, training dataset distribution and training algorithm have significant impact on overall accuracy of developed system [ 39 ]. We investigated two different neural network architectures, feedforward and feedback, for various number of neurons in hidden layer, which are according to the application experts, sufficient to properly perform the classification [ 15 , 40 – 42 ].…”

Section: Methodsmentioning

confidence: 99%

“…The usage of ANN in disease classification happens very often [ 15 – 27 ], though there have been only few studies investigating neural networks in genome-enabled predictions and classifications [ 28 , 29 ]. As for cytogenetic analysis, in recent years, several research groups have developed and tested different ANNs for the classification of metaphase chromosomes.…”

Section: Introductionmentioning

confidence: 99%

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Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

et al. 2018

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BackgroundThe usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work.MethodsThe database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound.ResultsThe architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%.ConclusionThe results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

et al. 2018

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“…Most of the COPD ML studies we reviewed have limited access to data, and their training datasets range from 16–300 patients. This may be one reason why none of the studies154,155 reported testing their algorithm in a real-world clinical setting, suggesting a gap between research in COPD ML algorithms and applications in daily clinical settings. To close this gap, a representative training dataset is needed.…”

Section: Systems Medicine Model For Copdmentioning

confidence: 99%

<p>Personalized medicine for patients with COPD: where are we?</p>

Franssen¹,

Alter²,

Bar³

et al. 2019

COPD

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Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.

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“…The literature shows limited work on AI and telemetry for respiratory disease monitoring [28] which means it is not as well understood as some other areas of study. However, some key explorations on the use of machine learning for respiratory disease diagnosis in clinical settings serve to effectively highlight the value of developing the technology [29][30][31]. Other external threats may impede the uptake of AI for respiratory disease management including the fear of lawsuits against the system creator if AI is proven to have made a poor judgement or an obvious mistake.…”

Section: Threatsmentioning

confidence: 99%

Artificial Intelligence for Long-term Respiratory Disease Management

Catherwood

Rafferty

McLaughlin

2018

Electronic Workshops in Computing

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This paper presents the strengths, weaknesses, opportunities, and threats for Artificial Intelligence (AI) as applied to long-term respiratory disease management. This analysis will help to identify, understand, and evaluate key aspects of the technology as well as the various internal/external forces which influence its success in this application space. Such understanding is instrumental to ensure judicial planning and implementation with suitable safeguards being considered. AI has the potential to radically change how respiratory disease management is conducted and may help clinicians to realise new treatment paradigms. The application of AI is clearly not specific to respiratory disease management; however it is a chronic disease that requires ongoing monitoring and well evidenced decision making regarding treatment pathways or medication modification. This work emphasises the current position of AI as applied to respiratory disease management and identifies the issues to help develop strategic directions to ensure successful implementation, evidenced by ubiquitous acceptance and uptake.

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Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease

Cited by 62 publications

References 35 publications

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

<p>Personalized medicine for patients with COPD: where are we?</p>

Artificial Intelligence for Long-term Respiratory Disease Management

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