Predicting special care during the COVID-19 pandemic: a machine learning approach

Bezzan, Vitor; Rocco, Cleber Damião

doi:10.1007/s13755-021-00164-6

Cited by 10 publications

(2 citation statements)

References 48 publications

(36 reference statements)

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“…The development of machine learning algorithms makes it feasible to learn from the available data for accessing the evolving risk factors and the newly exposed areas. Several studies have used supervised machine learning algorithms to identify patients at risk of developing severe COVID-19 symptoms [ 7 ]. For example, Assaf et al [ 8 ] predicted risk for critical COVID-19 based on status at admission using machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning

et al. 2022

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The worldwide spread of COVID-19 has caused significant damage to people’s health and economics. Many works have leveraged machine learning models to facilitate the control and treatment of COVID-19. However, most of them focus on clinical medicine and few on understanding the spatial dynamics of the high-risk population for transmission of COVID-19 in real-world settings. This study aims to investigate the association between population features and COVID-19 transmission risk in Hong Kong, which can help guide the allocation of medical resources and the implementation of preventative measures to control the spread of the pandemic. First, we built machine learning models to predict the number of COVID-19 cases based on the population features of different tertiary planning units (TPUs). Then, we analyzed the distribution of cases and the prediction results to find specific characteristics of TPUs leading to large-scale outbreaks of COVID-19. We further evaluated the importance and influence of various population features on the prediction results using SHAP values to identify indicators for high-risk populations for COVID-19 transmission. The evaluation of COVID-19 cases and the TPU dataset in Hong Kong shows the effectiveness of the proposed methods. The top three most important indicators are identified as people in accommodation and food services, low income, and high population density.

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Section: Introductionmentioning

confidence: 99%

Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning

et al. 2022

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“…COVID-19 10 , autism spectrum disorder 11 , cancer 12 , multiple sclerosis 13 , diabetes 14 and mental health 15 . Vitor and Cleber 16 developed an ML model to predict COVID-19 patients' stay at special care facilities, based on physiological features resulting in a decision system, which showed potential to be applied in several different diseases, with low processing requirements. While most of these studies utilize patients' physical and physiological data, derived from different biosensors, there are only a few handful studies, focused on developing a highly accurate biosensor.…”

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confidence: 99%

Machine learning for detecting DNA attachment on SPR biosensor

Mondal

Ahmed

Birbilis

et al. 2023

Sci Rep

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Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

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Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients

Alhawas

Kartal

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Predicting special care during the COVID-19 pandemic: a machine learning approach

Cited by 10 publications

References 48 publications

Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning

Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning

Machine learning for detecting DNA attachment on SPR biosensor

Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients

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