Mining Sequential Risk Patterns from Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease

Cheng, Yi-Ting; Lin, Yu-Feng; Chiang, Kuo‐Hwa; Tseng, Vincent S.

doi:10.1109/jbhi.2017.2657802

Cited by 20 publications

(13 citation statements)

References 19 publications

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“…Discrete individual and different classifier coordination were used to make exact strategy for sickness recording. [23] Rich game plan of continuous standards are used for the portrayal figuring. Least help check is used to organize the progressive models.…”

Section: Related Workmentioning

confidence: 99%

Liver Disease Classification using Deep Learning Algorithm

Anand¹,

Neelanarayanan²

2019

IJITEE

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Data Mining is one of the prevalent elucidating portions of programmed request and distinguishing proof. It involves data mining counts and strategies to examine helpful data. Of late, liver dissents have disproportionately expanded and liver infections are complimenting one of the most human pains in different countries. Early assurance of Liver Disorder is essential for the welfare of human culture. This complaint should be considered sincerely by setting up watchful structures for the early break down and expectation of Liver contaminations. The robotized gathering system suffers with non attendance of precision results when differentiated and cautious biopsy. We propose another model for liver issue request for separating the patient's helpful, data using ANN algorithm. The remedial records are organized whether there is a believability of essence of disorder or not. This proposed methodology uses extracted features using M-PSO and ANN for classifying the features. The ANN methodology improves the accuracy when appeared differently in relation to existing request computations. This paper focuses classification of selected features for classification.

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Section: Related Workmentioning

confidence: 99%

Liver Disease Classification using Deep Learning Algorithm

Anand¹,

Neelanarayanan²

2019

IJITEE

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“…EHRs often contain information on vital signs, laboratory results, clinical records, and previous medical care, which have been widely used in medical research and can be used to support a clinician's decision as part of early screening and diagnosis programs, or to predict disease progression and risk factors. 12 , 13 EHRs may provide complementary features for image data, and increase model interpretability. Several risk factors for DR, as determined by previous cross-sectional studies, can be retrieved from a patient's EHR, such as age, gender, body mass index (BMI), diabetes history, hypertension history, glycosylated hemoglobin (HbA1c), and systolic blood pressure.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions

Hsu

Chiou

Liu

et al. 2021

Trans. Vis. Sci. Tech.

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Purpose Fundus images are typically used as the sole training input for automated diabetic retinopathy (DR) classification. In this study, we considered several well-known DR risk factors and attempted to improve the accuracy of DR screening. Metphods Fusing nonimage data (e.g., age, gender, smoking status, International Classification of Disease code, and laboratory tests) with data from fundus images can enable an end-to-end deep learning architecture for DR screening. We propose a neural network that simultaneously trains heterogeneous data and increases the performance of DR classification in terms of sensitivity and specificity. In the current retrospective study, 13,410 fundus images and their corresponding nonimage data were collected from the Chung Shan Medical University Hospital in Taiwan. The images were classified as either nonreferable or referable for DR by a panel of ophthalmologists. Cross-validation was used for the training models and to evaluate the classification performance. Results The proposed fusion model achieved 97.96% area under the curve with 96.84% sensitivity and 89.44% specificity for determining referable DR from multimodal data, and significantly outperformed the models that used image or nonimage information separately. Conclusions The fusion model with heterogeneous data has the potential to improve referable DR screening performance for earlier referral decisions. Translational Relevance Artificial intelligence fused with heterogeneous data from electronic health records could provide earlier referral decisions from DR screening.

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“…Massive medical data sets contain wealthy domain knowledge that can help physicians in decision-making. Some previous research, based on the study of semantic knowledge underlying from medical corpus, has been undertaken to improve the assessment and management of disease [4,5]. These studies have helped to improve accuracy in disease assessment and reduce errors in disease treatment.…”

Section: Introductionmentioning

confidence: 99%

Mining health knowledge graph for health risk prediction

et al. 2020

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Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients' personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients' health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the

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Mining Sequential Risk Patterns from Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease

Cited by 20 publications

References 19 publications

Liver Disease Classification using Deep Learning Algorithm

Liver Disease Classification using Deep Learning Algorithm

Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions

Mining health knowledge graph for health risk prediction

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