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• W4200138766 abstract "Bioinformatics and health informatics-in conjection with data science, data mining and machine learning-have been applied in numerous real-life applications including disease and healthcare analytics, such as predictive analytics of coronavirus disease 2019 (COVID-19). Many of these existing works usually require large volumes of data train the classification and prediction models. However, these data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. For instance, partially due to privacy concerns and other factors, the volume of available disease data can be limited. Hence, in this paper, we present a predictive analytics system to support health analytics. Specifically, the system make good use of autoencoder and few-shot learning to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples), which helps to support prediction on classification of potential patients (e.g., potential COVID-19 patients). Moreover, the system also provides users (e.g., healthcare providers) with predictions on hospitalization status and clinical outcomes of COVID-19 patients. This provides healthcare administrators and staff with a good estimate on the demand for healthcare support. With this system, users could then focus and provide timely treatment to the true patients, thus preventing them for spreading the disease in the community. The system is helpful, especially for rural areas, when sophisticated equipment (e.g., CT scanners) may be unavailable. Evaluation results on a real-life datasets demonstrate the effectiveness of our digital health system in health analytics, especially in classifying patients and their medical needs." @default.
• W4200138766 created "2021-12-31" @default.
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• W4200138766 date "2021-10-25" @default.
• W4200138766 modified "2023-10-18" @default.
• W4200138766 title "Predictive Analytics to Support Health Informatics on COVID-19 Data" @default.
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• W4200138766 cites W1993415883 @default.
• W4200138766 cites W2033172859 @default.
• W4200138766 cites W2066070009 @default.
• W4200138766 cites W207625154 @default.
• W4200138766 cites W2077981172 @default.
• W4200138766 cites W2191117394 @default.
• W4200138766 cites W2487898712 @default.
• W4200138766 cites W2575557354 @default.
• W4200138766 cites W2590683580 @default.
• W4200138766 cites W2676841717 @default.
• W4200138766 cites W2809044511 @default.
• W4200138766 cites W2890270754 @default.
• W4200138766 cites W2896262751 @default.
• W4200138766 cites W2900718729 @default.
• W4200138766 cites W2903930265 @default.
• W4200138766 cites W2913340405 @default.
• W4200138766 cites W2971073813 @default.
• W4200138766 cites W2992810907 @default.
• W4200138766 cites W3007479728 @default.
• W4200138766 cites W3007500409 @default.
• W4200138766 cites W3013050088 @default.
• W4200138766 cites W3016609671 @default.
• W4200138766 cites W3030419021 @default.
• W4200138766 cites W3031443331 @default.
• W4200138766 cites W3034560014 @default.
• W4200138766 cites W3034711653 @default.
• W4200138766 cites W3034942609 @default.
• W4200138766 cites W3038925693 @default.
• W4200138766 cites W3043442870 @default.
• W4200138766 cites W3046022368 @default.
• W4200138766 cites W3046563483 @default.
• W4200138766 cites W3048290924 @default.
• W4200138766 cites W3048620154 @default.
• W4200138766 cites W3081828099 @default.
• W4200138766 cites W3085986812 @default.
• W4200138766 cites W3087795675 @default.
• W4200138766 cites W3091820618 @default.
• W4200138766 cites W3092272546 @default.
• W4200138766 cites W3093272110 @default.
• W4200138766 cites W3093367571 @default.
• W4200138766 cites W3096295646 @default.
• W4200138766 cites W3102328262 @default.
• W4200138766 cites W3104333927 @default.
• W4200138766 cites W3106791009 @default.
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• W4200138766 cites W3115764869 @default.
• W4200138766 cites W3116573776 @default.
• W4200138766 cites W3118230897 @default.
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• W4200138766 doi "https://doi.org/10.1109/bibe52308.2021.9635556" @default.
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