A hybrid model for automatic identification of risk factors for heart disease

Yang, Hui; Garibaldi, Jonathan M.

doi:10.1016/j.jbi.2015.09.006

Cited by 71 publications

(34 citation statements)

References 25 publications

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“…The most widely used machine learning approach is SVMs, having been used for predicting heart disease in medical records [32,46], identifying EHR progress notes pertaining to diabetes [94], and categorizing breast radiology reports according to BI-RADS [22].…”

Section: Resultsmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

366

259

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Background Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations as keywords to maximize coverage of the articles. Results Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision . The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods...

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Section: Resultsmentioning

confidence: 99%

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Sheikhalishahi¹,

Miotto²,

Dudley³

et al. 2019

JMIR Med Inform

366

259

View full text Add to dashboard Cite

show abstract

“…For this purpose, an efficient evolutionary feature selection technique, Ruzzo-Tompa, is used. The idea is to select those features that contribute more to improving the performance of the system because, if all the features are given to the model, the noisy nature of some features might affect the performance of the model [21]. Additionally, the results will be best on the training set but not on the testing set [22].…”

Section: Resultsmentioning

confidence: 99%

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

et al. 2020

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A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%.

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“…Yang and Garibaldi [13] proposed an information extraction system that was developed to automatically identify risk factors for heart disease in medical records. The authors had relied on quite a few nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting.…”

Section: Related Workmentioning

confidence: 99%

Data Mining, Soft Computing, Machine Learning and BioInspired Computing for Heart Disease Classification/ Prediction – A Review

Rathi¹,

Narasimhan²

2017

IJARCSSE

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Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patient's life. In this survey research article we aim to portray various data mining algorithms, soft computing techniques, machine learning algorithms and bio-inspired algorithms for predicting / classifying heart disease. Several mechanisms namely apriori algorithm, frequent itemset mining, support vector machine, neural network, classification and regression trees, fuzzy rule-based clinical decision support system, k-nearest neighbor, genetic algorithm, scoring system, nature language processing (NLP) techniques, type-2 fuzzy logic system, decision tree and statistical methods are used to classify heart disease prediction. From this survey research, it is identified that support vector machine algorithm outperforms all the other methods in terms of classification accuracy.

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A hybrid model for automatic identification of risk factors for heart disease

Cited by 71 publications

References 25 publications

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

Data Mining, Soft Computing, Machine Learning and BioInspired Computing for Heart Disease Classification/ Prediction – A Review

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