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

ABSTRACT: Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language processing (NLP) techniques such as machine learning, rule-based methods, and dictionary-based keyword spotting to cope with complicated clinical contexts inherent in a wide variety of risk factors. Our system achieved encouraging performance on the challenge test data with an overall micro-averaged F-measure of 0.915, which was competitive to the best system (F-measure of 0.927) of this challenge task.

SUBMITTER: Yang H

PROVIDER: S-EPMC4989091 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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

Yang Hui H Garibaldi Jonathan M JM

Journal of biomedical informatics 20150912

Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describes an information extraction system that was developed to automatically identify risk factors for heart disease in medical records while the authors participated in the 2014 i2b2/UTHealth NLP Challenge. Our approaches rely on several nature language ...[more]

PMID: 26375492

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Project description:AimsInformation on the prevalence, outcome and factors associated with heart failure in patients with adult congenital heart disease (CHD) (ACHD-HF) is lacking. We aimed at assessing the prevalence and outcome of ACHD-HF, the variables associated with ACHD-HF, and the differences between major anatomical/pathophysiological ACHD subgroups.Methods and resultsWe included 3905 patients (age 35.4 ± 13.2 years) under active follow-up in our institution (last visit >2010). Outcome of ACHD-HF cases was compared with sex- and age-matched cases. Univariable and multivariable binary logistic regression with ACHD-HF diagnosis as a dependent variable was performed. Overall prevalence of ACHD-HF was 6.4% (mean age 49.5 ± 16.7 years), but was higher in patients with cyanotic CHD (41%), Fontan circulation (30%), and a systemic right ventricle (25%). All-cause mortality was higher in ACHD-HF cases when compared with controls (mortality rate ratio 4.67 (2.36-9.27); P = 0.0001). In multivariable logistic regression analysis, age at latest follow-up [per 10 years; odds ratio (OR) 1.52; 95% confidence interval (CI) 1.31-1.77], infective endocarditis (OR 4.11; 95%CI 1.80-9.38), history of atrial arrhythmia (OR 3.52; 95%CI 2.17-5.74), pacemaker implantation (OR 2.66; 95% CI 1.50-4.72), end-organ dysfunction (OR 2.41; 95% CI 1.03-5.63), New York Heart Association class (OR 9.28; 95% CI 6.04-14.25), heart rate (per 10 bpm; OR 1.27; 95% CI 1.08-1.50), ventricular dysfunction (OR 3.62; 95% CI 2.54-5.17), and pulmonary hypertension severity (OR 1.66; 95% CI 1.21-2.30) were independently related to the presence of ACHD-HF. Some variables (age, atrial arrhythmia, pacemaker, New York Heart Association, and ventricular dysfunction) were related to ACHD-HF in all anatomical/physiological subgroups, whereas others were not.ConclusionsACHD-HF is prevalent especially in complex CHD and is associated with poor prognosis. Our data provide insight in the factors related to ACHD-HF including differences between specific anatomical and physiological subgroups.

Project description:BackgroundCongenital heart disease (CHD), which involve congenital cardiovascular malformations that occur during an embryo stage, may be the result of a complex interaction between genetic factors and environmental factors. The homozygous 677 T/T MTHFR gene and potential factors have been associated with CHD. Our objective was to study associations between potential environmental risk factors and methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms in CHD.MethodsA total of 346 children with CHD and 237 healthy children were recruited. Their parents were also enlisted in the study and interviewed face-to-face to identify potential environmental risk factors. The MTHFR genotype was analyzed by restriction fragment length polymorphism (RFLP). Interactions between environmental risk factors and MTHFR gene polymorphisms were evaluated by the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S).ResultsThere were significant differences in the occupational statuses of the mothers and their levels of drug exposure during gestation between the controls and the cases (P<0.05). These differences significantly increased offspring CHD risk (occupation: OR =5.45, 95% CI: 3.46-8.58; drug exposure: OR =4.91, 95% CI: 2.18-11.09). The frequency of the MTHFR gene 677 TT polymorphism in the mothers who had offspring with CHD was significantly different from that in the non-CHD controls (P<0.05). The frequency of this polymorphism also significantly differed between the children with CHD and the control group (P<0.05). An interaction was identified between the presence of the homozygous 677 TT genotype in the children and the mothers' occupational statuses (RERI =9.43, CI: 0.06-18.91).ConclusionsA significant interaction was found between the homozygous 677 T/T MTHFR gene in children and the maternal occupational status and level of drug exposure during gestation. Avoiding or reducing the exposure of the risk factors mentioned above, strengthening pre-pregnancy checkups and guidance might help to reduce the risk of CHD.

Project description:The degree of production efficiency and the quality of the commodities produced may both be directly impacted by the presence of illnesses in tea leaves. These days, this procedure may be automated with the use of artificial intelligence tools, and a number of approaches have been put out to satisfy these needs. Nonetheless, current research efforts have focused on improving diagnosis accuracy and expanding the variety of illnesses that might affect tea leaves. In this article, a new method is proposed for accurately diagnosing tea leaf diseases using artificial intelligence techniques. In the proposed method, the input images are preprocessed to remove redundant information. Then, a hybrid pooling-based Convolutional Neural Network (CNN) is employed to extract image features. In this method, the pooling layers of the CNN model are randomly adjusted based on either max pooling or average pooling functions. This strategy can enhance the efficiency of the CNN-based feature extraction model. In this method, the pooling layers of the CNN model are randomly adjusted based on either max pooling or average pooling functions. This strategy can enhance the efficiency of the CNN-based feature extraction model. After feature extraction, a weighted Random Forest (WRF) model is used for the detection of tea leaf diseases. The outputs of the decision tree models and their corresponding weights are used to identify tea leaf illnesses in this classification model, where each tree in the random forest is given a weight depending on how well it performs. The Cuckoo Search Optimization (CSO) method is used in the proposed classification model to give a weight to each tree. Tea Sickness Dataset (TSD) has been used as the basis for evaluating the suggested method's effectiveness. The findings show that the suggested approach has an average accuracy of 92.47% in identifying seven different forms of tea leaf illnesses. Additionally, the recall and accuracy metrics indicate results of 92.35 and 92.26, respectively, indicating improvements over earlier techniques.

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

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

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