Výsledky vyhledávání - "Rachel Yesharim"

Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates

Autor: Michelle N. Meyer, Donna M. Wolk, Alon Lanyado, Rachel Yesharim, Joseph J. Doyle, Maheen Shermohammed, Christopher F. Chabris, Amir Goren

Publikováno v: medRxiv

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae0fe9f46a581ff4e5aa83ab5564ae6a
https://pubmed.ncbi.nlm.nih.gov/33655258

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Predicting individual risk for COVID19 complications using EMR data

Autor: Rachel Yesharim, Gabriel Chodick, Tamar Domany, Avi Shoshan, Yaron Kinar, Alon Lanyado, Varda Shalev

BackgroundThe global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is ev

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::018c64239f4402252ae572df182ada25
https://doi.org/10.1101/2020.06.03.20121574

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Prediction of progression from pre‐diabetes to diabetes: Development and validation of a machine learning model

Autor: Avivit Cahn, Varda Shalev, Avi Shoshan, Tal Sagiv, Rachel Yesharim, Ran Goshen, Itamar Raz

Publikováno v: Diabetes/Metabolism Research and Reviews. 36

AIMS Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whether

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::068c860d678e14e284e364a4d28ee7cf
https://doi.org/10.1002/dmrr.3252

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Use of a Machine Learning Algorithm Improves Prediction of Progression to Diabetes

Autor: Itamar Raz, Avivit Cahn, Avi Shoshan, Rachel Yesharim, Ran Goshen, Tal Sagiv

Publikováno v: Diabetes. 67

Identification a-priori of subjects at high risk of progression from prediabetes to diabetes may enable targeted delivery of interventional programs, while avoiding the burden of prevention and treatment in those at low risk. This study relies on the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4957615caef79062d170e648a4a11307
https://doi.org/10.2337/db18-1286-p

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