Feature-maximum-dependency-based fusion diagnosis method for COPD

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

doi:10.1007/s11042-018-6876-6

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…For clinical time series, researchers often use machine learning methods to mine electronic medical record data. Karaolis 25 used the C4.5 decision tree method, and Fang 26 used the random forest method to mine and analyze data on Chronic Obstructive Pulmonary Disease (COPD), successfully assessing risk factors. The logistic regression method can balance the accuracy and interpretability of the model and is widely used in the diagnosis and prediction of diseases such as Alzheimer's disease 27 and cancer 28 .…”

Section: Related Workmentioning

confidence: 99%

Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events

Yang

Wang

2022

Concurrency and Computation

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Large collections of electronic clinical data today provide us with a vast source of information on medical practice. However, the utilization of those data for exploratory analysis to support clinical decisions is still limited. It is particularly challenging for extracting useful disease progression patterns from such data because it is longitudinal, incomplete, irregular, and heterogeneous of the patient conditions. In this article, we propose an integrated clinical event prediction model medical concept integrated residual short-long temporal convolutional networks (SL-TCN) to address these challenges. Compared to existing models, our model has three-fold advantages: (1) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence. ( 2) it learns a continuous-time progression model from discrete-time observations with nonequal intervals and high-dimensional features. (3) We fuse the temporal convolutional network, the long short-term memory network, and the residual connector so as to capture the local and global dependency of the sequence and make the clinical event predictions more robust. Through extensive experiments on the MIMIC III dataset, we demonstrate that our SL-TCN achieves higher precision in clinical event prediction and derives some interesting clinical insights.

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Section: Related Workmentioning

confidence: 99%

Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events

Yang

Wang

2022

Concurrency and Computation

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“…We used medical test results and symptoms to predict whether patients have COPD and to classify the diseases. To verify the effectiveness of the BPD algorithm based on the instance and feature transfers, we performed experiments on the following two datasets: the COPD dataset provided by the Clinical Medical Science Data Center and the COPD dataset extracted from the electronic medical records of a partner medical system [42]. The test set was the COPD dataset obtained from the electronic medical document of the partner medical system.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…A total of 1200 pieces of data were extracted from the COPD dataset from the electronic medical records of the partner healthcare system [42]; this included two classes of 750 COPD patients and 450 non-COPD patients who had symptoms similar to COPD patients. Table 4 gives the original 26 feature descriptions extracted from the electronic medical record.…”

Section: A Experimental Datasetmentioning

confidence: 99%

Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning

Wang

et al. 2020

IEEE Access

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Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously endangers human health and has high incidence and mortality worldwide. Therefore, an effective predictive model is required for COPD diagnosis. Given the limited data samples available in current COPD studies, we propose a method for diagnosing COPD based on transfer learning called balanced probability distribution (BPD) algorithm; this algorithm integrates instance-and feature-based transfers to improve the prediction accuracy of the model. First, instance-based cascaded transfer learning was used to initialize the weight distribution of the training data and obtain instances closer to the target domain. Second, the crossdomain feature filtering algorithm was adopted to filter irrelevant features, eliminate redundant features, and obtain the co-occurrence features of the source and target domains. Moreover, the remaining features were assigned different weights and transformed into the same space to reduce the distribution difference between the domains. Third, the BPD algorithm was used to balance the examples and the co-occurrence features from multiple disease source domains and construct a more suitable classification model of the target domain. Finally, the elastic network was used to further improve the generalization performance of the model. The experimental results show that the prediction effect of the BPD model is better than that of state-of-the-art methods and has strong generalization ability and robustness. We proved that our proposed BPD method works well in the COPD prediction model when the sample size is small. INDEX TERMS Balanced probability distribution (BPD) algorithm, chronic obstructive pulmonary disease (COPD), feature extraction, few-shot learning, transfer learning.

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Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network

et al. 2024

IJCESEN

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Recently, symptoms of Chronic Obstructive Pulmonary Disease (COPD) have been identified concerning long-term continuous treatment. Furthermore, predicting the life probability of patients with COPD is crucial for formative ensuing treatment and conduct plans. Additionally, it plays a vital role in providing complementary solutions using technologies such as Deep Learning (DL) to address experiments in the medical field. Early and timely analysis of clinical images can improve prognostic accuracy. These include COPD, pneumonia, asthma, tuberculosis and fibrosis. Conventional methods of diagnosing COPD often rely on physical exams and tests such as spirometers, chest and genetic analysis. However, respiratory diseases pose an enormous comprehensive health burden for many patients. Thus these methods are not always accurate or obtainable. However, succeeding in their accuracy involves a nonspecific diagnosis rate, time-consuming manual procedures, and extensive clinical imaging knowledge of the radiologist. To solve this problem, we use a Deep Recursive Convolutional Neural Network (DRCNN) method to detect chronic lower respiratory disease. Initially, we collected the images from the Kaggle repository, and evaluate the result based on the following stage. The first stage is pre-processing using a Gaussian filter to reduce noise and detect the edges. The second stage is segmentation used on Image Threshold Based Segmentation (ITBS), used for counting the binary image and separating the regions. In the third stage, we use the chi-square test to select the best features and evaluate the image values for each feature and threshold. Finally, classification using DRCNN detects CLRD classifying better than the previous method. In synthesis, CLRD can be detected by many staging measures, such as sensitivity, specificity, accuracy, precision, and Recall

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Feature-maximum-dependency-based fusion diagnosis method for COPD

Cited by 3 publications

References 17 publications

Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events

Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events

Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning

Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network

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