Disease state prediction for 2019 novel coronavirus (COVID-19) pneumonia using CT manifestations and body temperature dynamic analysis

The unprecedented outbreak of novel coronavirus 2019 (COVID-19) globally has a huge impact to our daily life in numerous ways. To effectively minimize the spread of the virus, early symptom detection is crucial, especially in closed environment with high human traffic areas which post higher chances of human-to-human transmission. Body temperature measurement has been identified among the vital monitoring parameters. However, current available temperature monitoring mechanism is costly, limited to single individual and limited to locally without integrating to cloud and database. This led to difficulty in effective surveillance for suspicious COVID cases. Hence, the purpose of this paper is to introduce an end-to-end Internet of Things-enabled application for thermal monitoring as an early signal detection and screening method. This work integrates Raspberry Pi, thermal sensor, LCD display, buzzer, and LED light with Raspbian and Restful API for device-to-cloud communication. The system implemented is capable for user identification, body temperature remote monitoring and warning signal for fever symptoms. The result of this real-time system is capable to detect and screen the suspected contagious person in an organization effectively. Future works on integrating face recognition with machine learning and artificial intelligent enhancement.

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Clinical Characteristics on Admission Predict In-Hospital Fatal Outcome in Patients Aged ≥75 Years with novel coronavirus disease (COVID-19): A Retrospective Cohort Study

10.21203/rs.3.rs-36077/v3 ◽

2020 ◽

Author(s):

Zhihua Yu ◽

Yuhe Ke ◽

Jiang Xie ◽

Hao Yu ◽

Wei Zhu ◽

...

Keyword(s):

Risk Factors ◽

Body Temperature ◽

Clinical Characteristics ◽

Troponin I ◽

Fatal Outcome ◽

High Sensitivity ◽

Lactic Dehydrogenase ◽

Hospital Death ◽

Laboratory Findings ◽

Novel Coronavirus

Abstract Background：Novel coronavirus disease（COVID-19）has become a worldwide pandemic and precise fatality data by age group are needed urgently. This study to delineate the clinical characteristics and outcome of COVID-19 patients aged ≥75 years and identify the risk factors of in-hospital death.Methods：A total of 141 consecutive patients aged ≥75 years who were admitted to the hospital between 12th and 19th February 2020. In-hospital death, clinical characteristics and laboratory findings on admission were obtained from medical records. The final follow-up observation was 31st March 2020.Results：The median age was 81 years (84 female, 59.6%). Thirty-eight (27%) patients were classified as severe or critical cases. 18 (12.8%) patients had died in hospital and the remaining 123 were discharged. Patients who died were more likely to present with fever (38.9% vs. 7.3%); low percutaneous oxygen saturation(SpO2) (55.6% vs. 7.3%); reduced lymphocytes (72.2% vs. 35.8%) and platelets (27.8% vs. 4.1%); and increased D-dimer (94.4% vs. 42.3%), creatinine (50.0% vs. 22.0%), lactic dehydrogenase (LDH) (77.8% vs. 30.1%), high sensitivity troponin I (hs-TnI) (72.2% vs. 14.6%), and N-terminal pro-brain natriuretic peptide (NT-proBNP) (72.2% vs. 6.5%; all P<0.05) than patients who recovered. Male sex (odds ratio [OR]=13.1, 95% confidence interval[CI] 1.1 to 160.1, P=0.044), body temperature >37.3°C (OR=80.5, 95% CI 4.6 to 1407.6, P=0.003), SpO2≤90% (OR=70.1, 95% CI 4.6 to 1060.4, P=0.002), and NT-proBNP>1800ng/L (OR=273.5, 95% CI 14.7 to 5104.8, P<0.0001) were independent risk factors of in-hospital death. Conclusions：In-hospital fatality among COVID-19 patients can be estimated by sex and on-admission measurements of body temperature, SpO2, and NT-proBNP.

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Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity

PLoS ONE ◽

10.1371/journal.pone.0014277 ◽

2010 ◽

Vol 5 (12) ◽

pp. e14277 ◽

Cited By ~ 44

Author(s):

Gopikrishna Deshpande ◽

Zhihao Li ◽

Priya Santhanam ◽

Claire D. Coles ◽

Mary Ellen Lynch ◽

...

Keyword(s):

Support Vector Machine ◽

Resting State ◽

Brain Connectivity ◽

Disease State ◽

Support Vector ◽

State Prediction

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Disease state prediction from single-cell data using graph attention networks

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384449 ◽

2020 ◽

Cited By ~ 1

Author(s):

Neal Ravindra ◽

Arijit Sehanobish ◽

Jenna L. Pappalardo ◽

David A. Hafler ◽

David van Dijk

Keyword(s):

Single Cell ◽

Disease State ◽

Attention Networks ◽

State Prediction ◽

Cell Data

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Investigation on the Application Value of Infrared Forehead Temperature Gun in Body Temperature Screening of New Crown Epidemic

Modern Electronic Technology ◽

10.26549/met.v5i1.6506 ◽

2021 ◽

Vol 5 (1) ◽

pp. 20

Author(s):

Kuanlong Shi ◽

Jiaxi Zhang ◽

Jiawen Wang

Keyword(s):

Body Temperature ◽

Temperature Measurement ◽

Life Safety ◽

The Novel ◽

Social Production ◽

Temperature Detection ◽

Measurement Results ◽

Novel Coronavirus

The outbreak of the novel coronavirus pneumonia has had a great impact on the life safety of our people and social production activities. Therefore, it is very important and meaningful to analyze the application value of infrared forehead thermometers in body temperature screening under the new crown pneumonia epidemic and propose improved measures for body temperature detection. This paper summarizes the questionnaire on the application value of infrared forehead thermometer in body temperature screening and the results of staff interviews, and analyzes the principle of infrared forehead thermometer temperature measurement to explain the factors that affect the accuracy of temperature measurement results. Finally, it is concluded that the reflected radiation of the environment and the temperature measurement distance affect the accuracy of the temperature measurement results of the infrared forehead gun.

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Clinical Characteristics on Admission Predict In-Hospital Fatal Outcome in Patients Aged ≥75 Years with novel coronavirus disease (COVID-19) :A Retrospective Cohort Study

10.21203/rs.3.rs-36077/v1 ◽

2020 ◽

Author(s):

Zhihua Yu ◽

Yuhe Ke ◽

Jiang Xie ◽

Hao Yu ◽

Wei Zhu ◽

...

Keyword(s):

Risk Factors ◽

Body Temperature ◽

Clinical Characteristics ◽

Troponin I ◽

Fatal Outcome ◽

High Sensitivity ◽

Lactic Dehydrogenase ◽

Hospital Death ◽

Laboratory Findings ◽

Novel Coronavirus

Abstract Background：Novel coronavirus disease（COVID-19）has become a worldwide pandemic and precise fatality data by age group are needed urgently. This study to delineate the clinical characteristics and outcome of COVID-19 patients aged ≥75 years and identify the risk factors of in-hospital death.Methods：A total of 141 consecutive patients aged ≥75 years who were admitted to the hospital between 12th and 19th February 2020. In-hospital death, clinical characteristics and laboratory findings on admission were obtained from medical records. The final follow-up observation was 31st March 2020.Results：The median age was 81 years (84 female, 59.6%). Thirty-eight (27%) patients were classified as severe or critical cases. 18 (12.8%) patients had died in hospital and the remaining 123 were discharged. Patients who died were more likely to present with fever (38.9% vs. 7.3%); low percutaneous oxygen saturation(SpO2) (55.6% vs. 7.3%); reduced lymphocytes (72.2% vs. 35.8%) and platelets (27.8% vs. 4.1%); and increased D-dimer (94.4% vs. 42.3%), creatinine (50.0% vs. 22.0%), lactic dehydrogenase (LDH) (77.8% vs. 30.1%), high sensitivity troponin I (hs-TnI) (72.2% vs. 14.6%), and N-terminal pro-brain natriuretic peptide (NT-proBNP) (72.2% vs. 6.5%; all P<0.05) than patients who recovered. Male sex (odds ratio [OR]=13.1, 95% confidence interval[CI] 1.1 to 160.1, P=0.044), body temperature >37.3°C (OR=80.5, 95% CI 4.6 to 1407.6, P=0.003), SpO2≤90% (OR=70.1, 95% CI 4.6 to 1060.4, P=0.002), and NT-proBNP>1800ng/L (OR=273.5, 95% CI 14.7 to 5104.8, P<0.0001) were independent risk factors of in-hospital death. Conclusions：In-hospital fatality among COVID-19 patients can be estimated by sex and on-admission measurements of body temperature, SpO2, and NT-proBNP.

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