Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Amal, Saeed; Safarnejad, Lida; Omiye, Jesutofunmi A.; Ghanzouri, Ilies; Cabot, John; Ross, Elsie Gyang

doi:10.3389/fcvm.2022.840262

Cited by 64 publications

(24 citation statements)

References 33 publications

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“…Recent advances in the field of machine learning have improved the discovery of new biomarkers for disease diagnosis or helped in designing treatment plans [ 14 , 25 , 26 , 27 ]. Dundar et al [ 28 ] utilized a proposed machine-learning surgical planning and found that it significantly contributed to positive outcomes for neurosurgery.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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“…The goal of this research field is to incorporate the unlabeled data from the occupational injury report such as text narratives and injury images with the labeled data to improve the predictive capabilities of the model [ 55 ]. As suggested by [ 56 ], the use of the deep learning method using the Generative Adversarial Network (GAN) can assist in medical diagnosis with full utilization of labeled and unlabeled data.…”

Section: Discussionmentioning

confidence: 99%

Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance

Khairuddin

Hui

Hasikin‬

et al. 2022

IJERPH

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Forecasting the severity of occupational injuries shall be all industries’ top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; ‘nature of injury’, ‘type of event’, and ‘affected body part’ in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.

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“…These notes are extracted from the narrative reports prepared by the Safety and Health Engineer and Occupational Health Doctor. This is the highlight of using multimodal data learning since it necessitates the integration of expertise in domain knowledge and technical aspects ( 56 , 69 ), enhancing the successful applications of the predictive model of occupational injury outcomes.…”

Section: Discussionmentioning

confidence: 99%

Predicting occupational injury causal factors using text-based analytics: A systematic review

Khairuddin

Hasikin‬

Razak

et al. 2022

Front. Public Health

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Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learningbased Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of , out of articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making

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Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Cited by 64 publications

References 33 publications

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance

Predicting occupational injury causal factors using text-based analytics: A systematic review

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