Diagnosis of COPD Based on a Knowledge Graph and Integrated Model

Fang, Youli; Wang, Hong; Wang, Lutong; Di, Ruitong; Song, Yongsoo

doi:10.1109/access.2019.2909069

Cited by 31 publications

(19 citation statements)

References 28 publications

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“…These parameters were not further utilized by the researcher to classify the COPD and pneumonia LS [ 20 ]. Nevertheless, the number of extracted features in some existing research works is less than the proposed technique but it can only identify a single pulmonary illness from LS analysis [ 6 , 12 , 18 , 25 , 26 ]. Details are presented in Table 8 .…”

Section: Resultsmentioning

confidence: 99%

“…If any symptom or feature is reported due to human error then the proposed system is unable to diagnose properly. It emerges the need for an automatic, economic, and non-invasive system for the diagnosis of COPD [ 25 , 26 , 27 ]. Another study is made for the identification of pneumonia and asthma subjects from cough sound analysis, specifically focused on the pediatric population.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Naqvi

Choudhry

2020

Sensors

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Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Naqvi

Choudhry

2020

Sensors

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“…Due to professionalism in the medical field, the IDF needs to be smoothed so that domain words that do not appear in all documents can also obtain a suitable IDF value. As shown in formula (13):…”

Section: Document Encodermentioning

confidence: 99%

“…Therefore, medical knowledge is indispensable in the diagnosis process. Fang et al [13] proposed a method to diagnose chronic obstructive pulmonary disease (COPD) based on a knowledge graph and integrated models. Liang et al [1] designed a system framework for the data mining of EMRs based on paediatric diseases.…”

Section: Introductionmentioning

confidence: 99%

Combining External Medical Knowledge for improving Obstetric Intelligent Diagnosis

Zhang

Cai

et al. 2020

Preprint

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Background: Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, how to make full use of EMRs and medical knowledge in intelligent diagnosis is worth studying.Methods: As an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multi-label classification task. We propose a novel neural network model called Knowledge-aware Hierarchical Diagnosis Model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In the KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis.Results: We evaluate our model on a real-world Chinese obstetric EMR dataset show that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods.Conclusion: In this paper, an improved model combining medical knowledge and an attention mechanism is proposed, which is based on the problem of the diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis, and we also verify the model's interpretability advantage.

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“…It can express more information than traditional knowledge-based methods. This method is mainly used in natural language processing, medical [11], financial and other fields. However, the application of this method in complex industrial fields is rare.…”

Section: Related Workmentioning

confidence: 99%

Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

Sun¹,

Wang²

2019

International Conferences on Software Engineering and Knowledge Engineering

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In complex industrial processes, complex associations are often involved. This complex relationship makes traditional fault detection and diagnosis methods difficult to achieve satisfactory results in multi-source fault detection and diagnosis. Therefore, this paper proposes a new multi-source fault detection and diagnosis framework. The method can successfully detect the state of the system, and at the same time, can locate the fault source simply and quickly. This method firstly constructs the multi-level knowledge graph in the complex industrial process, and then use the discriminant coefficient R to detect whether the system has failed. If the system fails into the fault diagnosis stage, the probability of the fault is derived based on Bayesian theory. This article describes the framework in detail. The TE process is taken as an example to prove the effectiveness of the method. Keywords-complex industrial process, knowledge graph, bayesian theory, fault detection and diagnosisI.

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Diagnosis of COPD Based on a Knowledge Graph and Integrated Model

Cited by 31 publications

References 28 publications

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Combining External Medical Knowledge for improving Obstetric Intelligent Diagnosis

Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

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