Accurate COVID-19 Health Outcome Prediction and Risk Factors Identification through an Innovative Machine Learning Framework Using Longitudinal Electronic Health Records

Feng, Alice

doi:10.1109/ichi52183.2021.00099

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The established representations According to Pratima Kumari et al, 22 are used to predict the number of rising confirmed cases, frequent confirmed cases, and increasing mortality cases. The output of this model may be utilized to plan and provide extra health decision‐making processes, as revealed by Narayana Darapaneni et al 23 According to Alice Feng et al, a model that can achieve AUROC of roughly 0.8–0.84 and RMSE of 5.7–1.5 for people who are hospitalized and spend time in the intensive care unit, as well as its longitudinal EHR statistics, can be effectively used to provide a comprehensive forecast of a person's risk to their wellbeing based on past histories of their physical conditions 24 …”

Section: Literature Reviewmentioning

confidence: 99%

“…The output of this model may be utilized to plan and provide extra health decision-making processes, as revealed by Narayana Darapaneni et al 23 According to Alice Feng et al, a model that can achieve AUROC of roughly 0.8-0.84 and RMSE of 5.7-1.5 for people who are hospitalized and spend time in the intensive care unit, as well as its longitudinal EHR statistics, can be effectively used to provide a comprehensive forecast of a person's risk to their wellbeing based on past histories of their physical conditions. 24 In the active, death, and recovered instances provided by polynomial LASSO and polynomial LR, Vartika Bhadana et al observed that this ML model offered the best outcome. Because of the ups and downs in dataset values, SVM displays inferior results overall.…”

Section: Literature Reviewmentioning

confidence: 99%

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Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

Yenurkar,

Mal,

Nyangaresi

et al. 2023

Engineering Reports

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AI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%