TY - GEN
T1 - Selection Bias from Data Processing in N3C
AU - N3C Consortium
AU - Haghighathoseini, Atefehsadat
AU - Qodrati, Mohammad
AU - Min, Hua
AU - Leslie, Timothy
AU - Frankenfeld, Cara
AU - Menon, Nirup M.
AU - Wojtusiak, Janusz
AU - Wilcox, Adam B.
AU - Lee, Adam M.
AU - Graves, Alexis
AU - Anzalone, Alfred
AU - Manna, Amin
AU - Saha, Amit
AU - Olex, Amy
AU - Zhou, Andrea
AU - Williams, Andrew E.
AU - Southerland, Andrew
AU - Girvin, Andrew T.
AU - Walden, Anita
AU - Sharathkumar, Anjali A.
AU - Amor, Benjamin
AU - Bates, Benjamin
AU - Hendricks, Brian
AU - Patel, Brijesh
AU - Alexander, Caleb
AU - Bramante, Carolyn
AU - Ward-Caviness, Cavin
AU - Madlock-Brown, Charisse
AU - Suver, Christine
AU - Chute, Christopher
AU - Dillon, Christopher
AU - Wu, Chunlei
AU - Schmitt, Clare
AU - Takemoto, Cliff
AU - Housman, Dan
AU - Gabriel, Davera
AU - Eichmann, David A.
AU - Mazzotti, Diego
AU - Brown, Don
AU - Boudreau, Eilis
AU - Hill, Elaine
AU - Zampino, Elizabeth
AU - Marti, Emily Carlson
AU - Pfaff, Emily R.
AU - French, Evan
AU - Koraishy, Farrukh M.
AU - Mariona, Federico
AU - Prior, Fred
AU - Sokos, George
AU - Martin, Greg
AU - Lehmann, Harold
AU - Spratt, Heidi
AU - Mehta, Hemalkumar
AU - Liu, Hongfang
AU - Sidky, Hythem
AU - Awori Hayanga, J. W.
AU - Pincavitch, Jami
AU - Clark, Jaylyn
AU - Harper, Jeremy Richard
AU - Islam, Jessica
AU - Ge, Jin
AU - Gagnier, Joel
AU - Saltz, Joel H.
AU - Saltz, Joel
AU - Loomba, Johanna
AU - Buse, John
AU - Mathew, Jomol
AU - Rutter, Joni L.
AU - McMurry, Julie A.
AU - Guinney, Justin
AU - Starren, Justin
AU - Crowley, Karen
AU - Bradwell, Katie Rebecca
AU - Walters, Kellie M.
AU - Wilkins, Ken
AU - Gersing, Kenneth R.
AU - Cato, Kenrick Dwain
AU - Murray, Kimberly
AU - Kostka, Kristin
AU - Northington, Lavance
AU - Pyles, Lee Allan
AU - Misquitta, Leonie
AU - Cottrell, Lesley
AU - Portilla, Lili
AU - Deacy, Mariam
AU - Bissell, Mark M.
AU - Clark, Marshall
AU - Emmett, Mary
AU - Saltz, Mary Morrison
AU - Palchuk, Matvey B.
AU - Haendel, Melissa A.
AU - Adams, Meredith
AU - Temple-O'Connor, Meredith
AU - Kurilla, Michael G.
AU - Morris, Michele
AU - Qureshi, Nabeel
AU - Safdar, Nasia
AU - Garbarini, Nicole
AU - Sharafeldin, Noha
AU - Sadan, Ofer
AU - Francis, Patricia A.
AU - Burgoon, Penny Wung
AU - Robinson, Peter
AU - Payne, Philip R.O.
AU - Fuentes, Rafael
AU - Jawa, Randeep
AU - Erwin-Cohen, Rebecca
AU - Patel, Rena
AU - Moffitt, Richard A.
AU - Zhu, Richard L.
AU - Kamaleswaran, Rishi
AU - Hurley, Robert
AU - Miller, Robert T.
AU - Pyarajan, Saiju
AU - Michael, Sam G.
AU - Bozzette, Samuel
AU - Mallipattu, Sandeep
AU - Vedula, Satyanarayana
AU - Chapman, Scott
AU - O'Neil, Shawn T.
AU - Setoguchi, Soko
AU - Hong, Stephanie S.
AU - Johnson, Steve
AU - Bennett, Tellen D.
AU - Callahan, Tiffany
AU - Topaloglu, Umit
AU - Sheikh, Usman
AU - Gordon, Valery
AU - Subbian, Vignesh
AU - Kibbe, Warren A.
AU - Hernandez, Wenndy
AU - Beasley, Will
AU - Cooper, Will
AU - Hillegass, William
AU - Zhang, Xiaohan Tanner
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study investigates potential selection bias in outcome prediction within the National COVID Cohort Collaborative (N3C) resulting from arbitrarily made decisions. In the processing of health data, decisions regarding cohort criteria and variable selection are often arbitrarily made, potentially introducing selection bias. This work explores if such decisions affect results of data analysis and potential conclusions of research studies. An experiment is conducted in which four arbitrary decisions are made. Results demonstrate significant differences in the obtained datasets and indicate a high potential for bias based on inclusion or exclusion decisions. The findings contribute to informed healthcare policies, better decision-making, and improved patient outcomes, emphasizing the necessity for testing assumptions and decisions in ongoing research that uses clinical data.
AB - This study investigates potential selection bias in outcome prediction within the National COVID Cohort Collaborative (N3C) resulting from arbitrarily made decisions. In the processing of health data, decisions regarding cohort criteria and variable selection are often arbitrarily made, potentially introducing selection bias. This work explores if such decisions affect results of data analysis and potential conclusions of research studies. An experiment is conducted in which four arbitrary decisions are made. Results demonstrate significant differences in the obtained datasets and indicate a high potential for bias based on inclusion or exclusion decisions. The findings contribute to informed healthcare policies, better decision-making, and improved patient outcomes, emphasizing the necessity for testing assumptions and decisions in ongoing research that uses clinical data.
KW - Data Processing
KW - National COVID Cohort Collaborative (N3C)
KW - Prediction
KW - Selection Bias
UR - http://www.scopus.com/inward/record.url?scp=85203704221&partnerID=8YFLogxK
U2 - 10.1109/ICHI61247.2024.00038
DO - 10.1109/ICHI61247.2024.00038
M3 - Conference contribution
AN - SCOPUS:85203704221
T3 - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
SP - 234
EP - 241
BT - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Healthcare Informatics, ICHI 2024
Y2 - 3 June 2024 through 6 June 2024
ER -