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COVID-19 GPH|Home|PHGKB Last data update: Oct 25, 2020 . (Total: 13059 Documents since 2020)
   

Last Posted: Oct-25-2020 05:33:09
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Digital diagnosis: Why teaching computers to read medical records could help against COVID-19
J Teo et al, World Economic Forum, October 21, 2020

Natural language processing (NLP) algorithms could find patterns across many thousands of patients’ records, helping to find effective treatments. They could also help to predict which patients are more likely to become seriously ill with COVID-19 - and predict upcoming surges of the pandemic.

Guide to Global Digital Tools for COVID-19 Response
CDC, November 23, 2020

The guide compares the District Health Information Software (DHIS2), the Surveillance, Outbreak Response Management and Analysis System (SORMAS), Go.Data, Open Data Kit (ODK), Epi Info, CommCare, KoboToolbox, Excel, and paper. Each has been deployed in various countries for contact tracing, investigations, and/or, in the case of DHIS2 and SORMAS, national surveillance.

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.
Clift Ash K et al. BMJ (Clinical research ed.) 2020 371m3731

QResearch database, comprising 1205 general practices in England was linked to covid-19 test results, Hospital Episode Statistics, and death registry data. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. For deaths from covid-19 in men, it explained 73.1% of the variation in time to death.

Susceptibility to severe COVID-19
DB Beck et al, Science, October 23, 2020

Many studies have focused on characterizing the heterogeneity of COVID-19 in terms of demographics, with clear evidence of higher mortality in men and older individuals. Host genetic risk factors have also emerged as a potential explanation for clinical heterogeneity and offer the potential for understanding molecular pathways for tailored therapeutic intervention.


news Latest News and Publications
Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis.
Sajal Md Sakibur Rahman, et al. Brain informatics 2020 10 0. (1) 12

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CLINICAL CHARACTERISTICS AND PROGNOSTIC FACTORS FOR ICU ADMISSION OF PATIENTS WITH COVID-19: A RETROSPECTIVE STUDY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING.
Izquierdo Jose Luis, et al. Journal of medical Internet research 2020 10 0.

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Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models.
Flesia Luca, et al. Journal of clinical medicine 2020 10 0. (10)

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Integrative analysis for COVID-19 patient outcome prediction.
Chao Hanqing, et al. Medical image analysis 2020 10 0. 101844

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Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan.
Zhu Xiaofeng, et al. Medical image analysis 2020 10 0. 101824

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Survival Analysis of COVID-19 Patients in Russia Using Machine Learning.
Metsker Oleg, et al. Studies in health technology and informatics 2020 9 0. 223-227

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An eHealth Platform for the Holistic Management of COVID-19.
Kouroubali Angelina, et al. Studies in health technology and informatics 2020 9 0. 182-188

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Clinical efficacy of chloroquine derivatives in COVID-19 infection: comparative meta-analysis between the big data and the real world.
Million M, et al. New microbes and new infections 2020 11 0. 100709

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The epigenetic implication in coronavirus infection and therapy.
Atlante Sandra, et al. Clinical epigenetics 2020 0 0. (1) 156

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The bumpy road to achieve herd immunity in COVID-19.
Neagu Monica, et al. Journal of immunoassay & immunochemistry 2020 10 0. 1-18

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About COVID-19 GPH

COVID-19 GPH is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that captures emerging discoveries and applications of genomics, molecular and other precision medicine and precision public health tools in the investigation and control of COVID-19. Contents include PubMed records via an automated pubmed search algorithm, preprint records from NIH iCite, the relevant information from many media sources picked by experts, and linkages to contents from our curated PHGKB databases.



Genomics Precision Health: The use of pathogen and human genomics and advanced molecular detection methods in discovery, clinical and public health investigations involving COVID-19.
Non Genomics Precision Health: The use of big data, data science and machine learning methods (not involving genomics) in discovery, clinical and public health investigations involving COVID-19

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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