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Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

2022; Dove Medical Press; Volume: Volume 14; Linguagem: Inglês

10.2147/clep.s323292

1179-1349

Kristin Kostka, Talita Duarte‐Salles, Albert Prats‐Uribe, Anthony G. Sena, Andrea Pistillo, Sara Khalid, Lana Yin Hui Lai, Asieh Golozar, Thamir M. Alshammari, Dalia Dawoud, Fredrik Nyberg, Adam B. Wilcox, Alan Andryc, Andrew E. Williams, Anna Ostropolets, Carlos Areia, Chi Young Jung, Christopher A. Harle, Christian Reich, Clair Blacketer, Daniel R. Morales, David A. Dorr, Edward Burn, Elena Roel, Eng Hooi Tan, Evan Minty, Frank DeFalco, Gabriel de Maeztu, Gigi Lipori, Heba Alghoul, Hong Zhu, Jason Thomas, Jiang Bian, Jimyung Park, Jordi Martínez Roldán, Jose Posada, Juan M. Banda, Juan Pablo Horcajada, Julianna Kohler, Karishma Shah, Karthik Natarajan, Kristine E. Lynch, Li Liu, Lisa M. Schilling, Martina Recalde, Henry M. Spotnitz, Mengchun Gong, Michael E. Matheny, Neus Valveny, Nicole G. Weiskopf, Nigam H. Shah, Osaid Alser, Paula Casajust, Rae Woong Park, Robert Schuff, Sarah Seager, Scott L. DuVall, Seng Chan You, Seok Young Song, Sergio Fernández‐Bertolín, Stephen Fortin, Tanja Magoč, Thomas Falconer, Vignesh Subbian, Vojtech Huser, Waheed‐Ul‐Rahman Ahmed, William Carter, Yin Guan, Yankuic Galvan, Xing He, Peter R. Rijnbeek, George Hripcsak, Patrick Ryan, Marc A. Suchard, Daniel Prieto‐Alhambra,

Machine Learning in Healthcare

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.