Non-contact screening system based for COVID-19 on XGBoost and logistic regression

Dong, Chunjiao; Qiao, Yixian; Shang, Chunheng; Liao, Xiwen; Yuan, Xianglin; Cheng, Qi; Li, Yuxuan; Zhang, Jianan; Wang, Yunfeng; Chen, Yahong; Ge, Qi; Yu-rong, Bao

doi:10.1016/j.compbiomed.2021.105003

Cited by 31 publications

(13 citation statements)

References 16 publications

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“…Although limited to supine position, Dong et al recently reported non-contact COVID-19 screening using continuous-wave radar-based non-contact sleep monitoring equipment via XGBoost and LRA model ( Dong et al, 2022 ). However, the VISC-Camera enables non-contact COVID-19 screening in standing and sitting postures, as well as in supine positions.…”

Section: Discussionmentioning

confidence: 99%

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation

Unursaikhan

Amarsanaa²,

Sun

et al. 2022

Front. Physiol.

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Background: To conduct a rapid preliminary COVID-19 screening prior to polymerase chain reaction (PCR) test under clinical settings, including patient’s body moving conditions in a non-contact manner, we developed a mobile and vital-signs-based infection screening composite-type camera (VISC-Camera) with truncus motion removal algorithm (TMRA) to screen for possibly infected patients.Methods: The VISC-Camera incorporates a stereo depth camera for respiratory rate (RR) determination, a red–green–blue (RGB) camera for heart rate (HR) estimation, and a thermal camera for body temperature (BT) measurement. In addition to the body motion removal algorithm based on the region of interest (ROI) tracking for RR, HR, and BT determination, we adopted TMRA for RR estimation. TMRA is a reduction algorithm of RR count error induced by truncus non-respiratory front-back motion measured using depth-camera-determined neck movement. The VISC-Camera is designed for mobile use and is compact (22 cm × 14 cm × 4 cm), light (800 g), and can be used in continuous operation for over 100 patients with a single battery charge. The VISC-Camera discriminates infected patients from healthy people using a logistic regression algorithm using RR, HR, and BT as explanatory variables. Results are available within 10 s, including imaging and processing time. Clinical testing was conducted on 154 PCR positive COVID-19 inpatients (aged 18–81 years; M/F = 87/67) within the initial 48 h of hospitalization at the First Central Hospital of Mongolia and 147 healthy volunteers (aged 18–85 years, M/F = 70/77). All patients were on treatment with antivirals and had body temperatures <37.5°C. RR measured by visual counting, pulsimeter-determined HR, and BT determined by thermometer were used for references.Result: 10-fold cross-validation revealed 91% sensitivity and 90% specificity with an area under receiver operating characteristic curve of 0.97. The VISC-Camera-determined HR, RR, and BT correlated significantly with those measured using references (RR: r = 0.93, p < 0.001; HR: r = 0.97, p < 0.001; BT: r = 0.72, p < 0.001).Conclusion: Under clinical settings with body motion, the VISC-Camera with TMRA appears promising for the preliminary screening of potential COVID-19 infection for afebrile patients with the possibility of misdiagnosis as asymptomatic.

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Section: Discussionmentioning

confidence: 99%

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation

Unursaikhan

Amarsanaa²,

Sun

et al. 2022

Front. Physiol.

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“…So, Dong, Qiao [103] proposed to use an ML method and a non-contact monitoring device to assess probable COVID-19 patients automatically. They detected breathing, sleep quality, body movement, heart rate, and a variety of other physiological indications using impulse-radio ultra-wideband radar.…”

Section: Monitoring and Tracking Methodsmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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“…The input of the binary classification model is an HLA-peptide pair, and the output of that is 1 or 0, where 1 means the peptide will bind to the HLA allele, and 0 means the peptide will not bind. Seven popular binary classifiers are used to establish the classification models, including logistic regression (LR) [ 39 ], support vector machine (SVM) [ 40 ], bagging classifier (Bagging) [ 41 ], extreme gradient boost (XGBoost) [ 42 ], k -nearest neighbor (KNN) [ 43 ], decision tree (Dtree) [ 44 ] and naive bayes (NB) [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Zhang

Zhu

et al. 2022

Briefings in Bioinformatics

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Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

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Non-contact screening system based for COVID-19 on XGBoost and logistic regression

Cited by 31 publications

References 16 publications

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation

Development of a Novel Vital-Signs-Based Infection Screening Composite-Type Camera With Truncus Motion Removal Algorithm to Detect COVID-19 Within 10 Seconds and Its Clinical Validation

Machine learning applications for COVID-19 outbreak management

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

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