[This retracts the article DOI: 10.1093/jamiaopen/ooad090.].
Author(s):
DOI: 10.1093/jamiaopen/ooae036
To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.
Author(s): Scott, Ian A, De Guzman, Keshia R, Falconer, Nazanin, Canaris, Stephen, Bonilla, Oscar, McPhail, Steven M, Marxen, Sven, Van Garderen, Aaron, Abdel-Hafez, Ahmad, Barras, Michael
DOI: 10.1093/jamiaopen/ooae031
[This corrects the article DOI: 10.1093/jamiaopen/ooae029.].
Author(s):
DOI: 10.1093/jamiaopen/ooae046
To address database interoperability challenges to improve collaboration among disparate organizations.
Author(s): DeFranco, Joanna F, Roberts, Joshua, Ferraiolo, David, Compton, D Chris
DOI: 10.1093/jamiaopen/ooae040
Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform.
Author(s): Balasubramanian, Jeya Balaji, Choudhury, Parichoy Pal, Mukhopadhyay, Srijon, Ahearn, Thomas, Chatterjee, Nilanjan, García-Closas, Montserrat, Almeida, Jonas S
DOI: 10.1093/jamiaopen/ooae055
Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.
Author(s): Kural, Kamil Can, Mazo, Ilya, Walderhaug, Mark, Santana-Quintero, Luis, Karagiannis, Konstantinos, Thompson, Elaine E, Kelman, Jeffrey A, Goud, Ravi
DOI: 10.1093/jamiaopen/ooae037
To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.
Author(s): Thayer, Daniel S, Mumtaz, Shahzad, Elmessary, Muhammad A, Scanlon, Ieuan, Zinnurov, Artur, Coldea, Alex-Ioan, Scanlon, Jack, Chapman, Martin, Curcin, Vasa, John, Ann, DelPozo-Banos, Marcos, Davies, Hannah, Karwath, Andreas, Gkoutos, Georgios V, Fitzpatrick, Natalie K, Quint, Jennifer K, Varma, Susheel, Milner, Chris, Oliveira, Carla, Parkinson, Helen, Denaxas, Spiros, Hemingway, Harry, Jefferson, Emily
DOI: 10.1093/jamiaopen/ooae049
To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.
Author(s): Nottke, Amanda, Alan, Sophia, Brimble, Elise, Cardillo, Anthony B, Henderson, Lura, Littleford, Hana E, Rojahn, Susan, Sage, Heather, Taylor, Jessica, West-Odell, Lisandra, Berk, Alexandra
DOI: 10.1093/jamiaopen/ooae041
Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts.
Author(s): Sikora, Andrea, Keats, Kelli, Murphy, David J, Devlin, John W, Smith, Susan E, Murray, Brian, Buckley, Mitchell S, Rowe, Sandra, Coppiano, Lindsey, Kamaleswaran, Rishikesan
DOI: 10.1093/jamiaopen/ooae033
The objective was to identify information loss that could affect clinical care in laboratory data transmission between 2 health care institutions via a Health Information Exchange platform.
Author(s): Luu, Hung S, Campbell, Walter S, Cholan, Raja A, Edgerton, Mary E, Englund, Andrea, Keller, Alana, Korte, Elizabeth D, Mitchell, Sandra H, Watkins, Greg T, Westervelt, Lindsay, Wyman, Daniel, Powell, Stephen
DOI: 10.1093/jamiaopen/ooae032