A Decision Support System for Chronic Obstructive Pulmonary Disease (COPD)

Iadanza, Ernesto; Mudura, Vlad Antoniu

doi:10.1007/978-981-10-9023-3_57

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The COPD Management Tool, which is a CDSS based on the C5.0 machine-learning algorithm, was developed to enable the interpretation and classification of the results of respiratory functional tests [29]. This instrument offers significant improvements in performance compared to the current state of the art in this sector.…”

Section: Resultsmentioning

confidence: 99%

An automatic system supporting clinical decision for chronic obstructive pulmonary disease

2019

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This paper presents a system supporting clinical decisions for patients with Chronic Obstructive Pulmonary Disease (COPD). The system should partially fill the gaps highlighted during an analysis of the current state of the art of Clinical Decision Support Systems (CDSS) for telemonitoring patients affected by COPD. The first step taken was to replicate the performance of similar decision support systems found in the scientific literature. Using physiological parameters drawn from respiratory function tests on 414 patients, two predictive models were created using two machine-learning algorithms: neural network and support vector machine. Performance was comparable to that described in the literature. The results made it possible to affirm that the data available were sufficient to evaluate the extent of respiratory deficit. The next step was to create a new predictive model with better performance than previously obtained. The C5.0 Machine Learning Algorithm was chosen for the development of the model. The resulting performance on the data available was significantly better than with the two previous models. This new predictive model, called COPD, was then implemented in a user interface created using Java programming language. The new software developed, which enables the evaluation and classification of respiratory test results and which can be used in many clinical applications, provides excellent performance compared to the current state of the art.

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Section: Resultsmentioning

confidence: 99%

An automatic system supporting clinical decision for chronic obstructive pulmonary disease

2019

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“…They declared which the proposed approach with excellent performance can be used in many clinical applications. 24 The limitation of this study is that to boost the adoption of artificial intelligence by the medical community, large-scale studies are required to validate present results and further evaluation of the prediction model is required in genuine primary care settings to expand the model generalizability.…”

Section: Discussionmentioning

confidence: 97%

“…ANN can be main tool for developing systems based on machine learning algorithms with the aim of monitoring the health status of individuals, correct diagnosis of the patient with pulmonary diseases and classification. 24,25 The MLP neural network model is a multilayered structure for modeling and prediction. The MLP network architecture three main layers: input, hidden, and output with many interconnected processing elements (PEs)…”

Section: Ann Modelmentioning

confidence: 99%

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

Kalantary

Pourbabaki

Jahani³

et al. 2021

Concurrency and Computation

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The spirometry is considered a preclinical tool for the evaluation of the respiratory system. The formal lung volumes measurement and health status lung system are made using spirometry. Artificial neural network (ANN) has been introduced in solving complex problems in a large number of different settings, including medical diagnosis support system as predictive power. An objective of this research was intended to investigate the development of a new decision support system (DSS) using ANN modeling approaches and algorithms to predict pulmonary function in people. The spirometry data and general characteristics, anthropometric data, and body composition parameters (N = 130) were obtained from subjects. The classification of pulmonary function was performed by the multi-layer perceptron (MLP) model. Findings show that the MLP model is capable of classifying respiratory abnormalities in different people. The ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. Also, a DSS tool was created that allows the evaluation and classification of the results of spirometry data. It appears that ANNs are useful in classification pulmonary function.

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“…[41][42][43][44][45][46] For example, a ML-based CDSS was developed for the management of patients with chronic obstructive pulmonary disease. 47 However, few studies have used ML algorithms to predict medication adherence behavior. Among the few relevant studies, random forest (RF) and boosted regression tree (BRT) have been applied to predict medication adherence among patients who were new to statin or statin combination drugs achieving cross-validated c-statistics of 0.81 and 0.842, respectively.…”

Section: Introductionmentioning

confidence: 99%

Predicting Lipid-Lowering Medication Persistence after the First Cardiovascular Disease Hospitalization

Warren

Exeter

2020

Methods Inf Med

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Objectives This study analyzed patient factors in medication persistence after discharge from the first hospitalization for cardiovascular disease (CVD) with the aim of predicting persistence to lipid-lowering therapy for 1 to 2 years. Methods A subcohort having a first CVD hospitalization was selected from 313,207 patients for proportional hazard model analysis. Logistic regression, support vector machine, artificial neural networks, and boosted regression tree (BRT) models were used to predict 1- and 2-year medication persistence. Results Proportional hazard modeling found significant association of persistence with age, diabetes history, complication and comorbidity level, days stayed in hospital, CVD diagnosis type, in-patient procedures, and being new to therapy. BRT had the best predictive performance with c-statistic of 0.811 (0.799–0.824) for 1-year and 0.793 (0.772–0.814) for 2-year prediction using variables potentially available shortly after discharge. Conclusion The results suggest that development of a machine learning-based clinical decision support tool to focus improvements in secondary prevention of CVD is feasible.

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A Decision Support System for Chronic Obstructive Pulmonary Disease (COPD)

Cited by 3 publications

References 8 publications

An automatic system supporting clinical decision for chronic obstructive pulmonary disease

An automatic system supporting clinical decision for chronic obstructive pulmonary disease

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

Predicting Lipid-Lowering Medication Persistence after the First Cardiovascular Disease Hospitalization

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