ABSTRACT
Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.
ABSTRACT
In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.
ABSTRACT
Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.
ABSTRACT
In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Embedded in these big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 610 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 2.5 years since COVID-19 has declared as a pandemic. As some of these cases require hospitalization. it is important to estimate the demand in hospitalization. Moreover, different levels of hospitalization may require different types of resources (e.g., hospital beds, medical staff). For example, patients admitted into the intensive care unit (ICU) may require assisted ventilation. Hence, in this paper, we present models to make predictions based on e-health records. Specifically, our binary model predicts whether a patient require hospitalization, whereas our multi-class model predicts what level of hospitalization (e.g., regular ward, semi-ICU, ICU) is required by the patient. Our models uses few-shot learning (and may use multi-task learning) with autoencoders (comprising encoders and decoders) and a predictor. Evaluation results on real-life e-health records show the practicality of our models in predicting hospital statuses of COVID-19 cases and the benefits of these models towards effective allocation of resources (e.g., hospital facilities, staff). © 2022 IEEE.