COVID-19 prediction using AI analytics for South Korea

Sinha, Adwitiya; Rathi, Megha

doi:10.1007/s10489-021-02352-z

Cited by 25 publications

(14 citation statements)

References 48 publications

(41 reference statements)

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“…The emergency department (ED) is the frontline for delivering emergency services in hospitals [1], and it is also the department with the most clustered patients and the heaviest rescue tasks [2]. The issue of patient congestion in the ED of hospitals in China is becoming increasingly severe, especially in the top three hospitals in Shanghai and other major cities, which has significantly affected the quality of medical services.…”

Section: Introductionmentioning

confidence: 99%

Forecasting patient arrivals at emergency department using calendar and meteorological information

Zhang

Tao

et al. 2022

Appl Intell

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Overcrowding in emergency departments (EDs) is a serious problem in many countries. Accurate ED patient arrival forecasts can serve as a management baseline to better allocate ED personnel and medical resources. We combined calendar and meteorological information and used ten modern machine learning methods to forecast patient arrivals. For daily patient arrival forecasting, two feature selection methods are proposed. One uses kernel principal component analysis(KPCA) to reduce the dimensionality of all of the features, and the other is to use the maximal information coefficient(MIC) method to select the features related to the daily data first and then perform KPCA dimensionality reduction. The current study focuses on a public hospital ED in Hefei, China. We used the data November 1, 2019 to August 31, 2020 for model training; and patient arrival data September 1, 2020 to November 31, 2020 for model validation. The results show that for hourly patient arrival forecasting, each machine learning model has better forecasting results than the traditional autoRegressive integrated moving average (ARIMA) model, especially long short-term memory (LSTM) model. For daily patient arrival forecasting, the feature selection method based on MIC-KPCA has a better forecasting effect, and the simpler models are better than the ensemble models. The method we proposed could be used for better planning of ED personnel resources. KeywordsEmergency department • Patient arrivals • Calendar and meteorological information • Kernel principal component analysis • Maximal information coefficient * Yan Zhang

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Section: Introductionmentioning

confidence: 99%

Forecasting patient arrivals at emergency department using calendar and meteorological information

Zhang

Tao

et al. 2022

Appl Intell

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“…The approach offers an automatic triage, testing visiting patients’ risk of SARS-CoV-2 through checking their underlined disease and their current immigration records to foreign countries. Sinha and Rathi reported that Korea uses artificial intelligence to predict if people infected with the virus survive [ 38 ]. The country achieves this by using machine learning with hyperparameters tuning, profound learning models, and an auto encoder-based method for valuing the effect of contrasting factors on the illness range and gauging the possibilities of survival for those in quarantine [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic—A Scoping Review

Taha‐Mehlitz¹,

Hendie

Taha

2021

JCM

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Background: SARS-CoV-2 has significantly transformed the healthcare environment, and it has triggered the development of electronic health and artificial intelligence mechanisms, for instance. In this overview, we concentrated on enhancing the two concepts in surgery after the pandemic, and we examined the factors on a global scale. Objective: The primary goal of this scoping review is to elaborate on how surgeons have used eHealth and AI before; during; and after the current global pandemic. More specifically, this review focuses on the empowerment of the concepts of electronic health and artificial intelligence after the pandemic; which mainly depend on the efforts of countries to advance the notions of surgery. Design: The use of an online search engine was the most applied method. The publication years of all the studies included in the study ranged from 2013 to 2021. Out of the reviewed studies; forty-four qualified for inclusion in the review. Discussion: We evaluated the prevalence of the concepts in different continents such as the United States; Europe; Asia; the Middle East; and Africa. Our research reveals that the success of eHealth and artificial intelligence adoption primarily depends on the efforts of countries to advance the notions in surgery. Conclusions: The study’s primary limitation is insufficient information on eHealth and artificial intelligence concepts; particularly in developing nations. Future research should focus on establishing methods of handling eHealth and AI challenges around confidentiality and data security.

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“…where, W hh refers to weight at recurrent neuron and W xh refers to weight at the input neuron. Finally, the output of the recurrent neural network is computed as following (5).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…This helped the covid-affected casualties to recover quickly, thereby minimizing the pace of disease progression. Meanwhile, the scientific community also started working towards designing prediction models to analyze the dynamics of corona pandemic and forecast its severity by using Artificial Intelligence (AI) analytics [5,6]. These AIbased models assisted various medical service providers, thereby allowing them to process medicinal demands, better scale-up, and other emergency healthcare delivery tasks.…”

Section: Introductionmentioning

confidence: 99%

Multi-class autoencoder-ensembled prediction model for detection of COVID-19 severity

Gupta

Sinha

2022

Evol. Intel.

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Coronavirus pandemic has hampered human life all over the world with several serious casualties and numerous deceased cases. Though the pace of the pandemic slowed down owing to the vaccination process, still new mutant variants of the virus continue to evolve. Our research aims on developing an Autoencoder (AE) based multi-class prediction model for detecting the severity of coronavirus. Our proposed model is based on certain symptoms, such as fever, dry cough, fatigue, difficulty in breathing, aches, sore throat, nasal congestion, diarrhea, etc. Our AI driven prediction model can efficiently identify distinct classes of severity, which includes no-infection, mild, moderate, and severe cases where a patient may require intensive medical care. We have considered machine learning models, including logistic regression, support vector machine, and decision trees; and recurrent deep neural network model for performance comparison with proposed Autoencoder (AE) based models. Our autoencoder-based logistic regression, decision trees, and support vector machine improves the prediction performance and accuracy towards anticipating the spread of coronavirus. It has been observed that AE-based deep learning models outperformed some of the existing machine learning and deep learning techniques with a maximum accuracy of 99.66%. Hence, AE-based models were found to be more suitable for multi-class prediction of large-scale patient-centric data in terms of F1-score, accuracy, precision, recall, model testing, training loss, and other validation statistics.

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COVID-19 prediction using AI analytics for South Korea

Cited by 25 publications

References 48 publications

Forecasting patient arrivals at emergency department using calendar and meteorological information

Forecasting patient arrivals at emergency department using calendar and meteorological information

The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic—A Scoping Review

Multi-class autoencoder-ensembled prediction model for detection of COVID-19 severity

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