AI in health: keeping the human in the loop.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad091
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad091
The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.
Author(s): Zhou, Yizhao, Shi, Jiasheng, Stein, Ronen, Liu, Xiaokang, Baldassano, Robert N, Forrest, Christopher B, Chen, Yong, Huang, Jing
DOI: 10.1093/jamia/ocad066
Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies.
Author(s): Alpi, Kristine M, Martin, Christie L, Plasek, Joseph M, Sittig, Scott, Smith, Catherine Arnott, Weinfurter, Elizabeth V, Wells, Jennifer K, Wong, Rachel, Austin, Robin R
DOI: 10.1093/jamia/ocad082
Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory [...]
Author(s): Ahalt, Stan, Avillach, Paul, Boyles, Rebecca, Bradford, Kira, Cox, Steven, Davis-Dusenbery, Brandi, Grossman, Robert L, Krishnamurthy, Ashok, Manning, Alisa, Paten, Benedict, Philippakis, Anthony, Borecki, Ingrid, Chen, Shu Hui, Kaltman, Jon, Ladwa, Sweta, Schwartz, Chip, Thomson, Alastair, Davis, Sarah, Leaf, Alison, Lyons, Jessica, Sheets, Elizabeth, Bis, Joshua C, Conomos, Matthew, Culotti, Alessandro, Desain, Thomas, Digiovanna, Jack, Domazet, Milan, Gogarten, Stephanie, Gutierrez-Sacristan, Alba, Harris, Tim, Heavner, Ben, Jain, Deepti, O'Connor, Brian, Osborn, Kevin, Pillion, Danielle, Pleiness, Jacob, Rice, Ken, Rupp, Garrett, Serret-Larmande, Arnaud, Smith, Albert, Stedman, Jason P, Stilp, Adrienne, Barsanti, Teresa, Cheadle, John, Erdmann, Christopher, Farlow, Brandy, Gartland-Gray, Allie, Hayes, Julie, Hiles, Hannah, Kerr, Paul, Lenhardt, Chris, Madden, Tom, Mieczkowska, Joanna O, Miller, Amanda, Patton, Patrick, Rathbun, Marcie, Suber, Stephanie, Asare, Joe
DOI: 10.1093/jamia/ocad048
As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue.
Author(s): Wang, Michelle, Sushil, Madhumita, Miao, Brenda Y, Butte, Atul J
DOI: 10.1093/jamia/ocad085
To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework.
Author(s): van der Vegt, Anton H, Scott, Ian A, Dermawan, Krishna, Schnetler, Rudolf J, Kalke, Vikrant R, Lane, Paul J
DOI: 10.1093/jamia/ocad075
The 21st Century Cures Act (Cures Act) information blocking regulations mandate timely patient access to their electronic health information. In most healthcare systems, this technically requires immediate electronic release of test results and clinical notes directly to patients. Patients could potentially be distressed by receiving upsetting results through an electronic portal rather than from a clinician. We present a case from 2018, several years prior to the implementation of the [...]
Author(s): Rotholz, Stephen, Lin, Chen-Tan
DOI: 10.1093/jamia/ocad074
We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds.
Author(s): Campbell, Ian M, Karavite, Dean J, Mcmanus, Morgan L, Cusick, Fred C, Junod, David C, Sheppard, Sarah E, Lourie, Eli M, Shelov, Eric D, Hakonarson, Hakon, Luberti, Anthony A, Muthu, Naveen, Grundmeier, Robert W
DOI: 10.1093/jamia/ocad070
Author(s):
DOI: 10.1093/jamia/ocad069
To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches are limited due to the scarcity of SCG data.
Author(s): Nikbakht, Mohammad, Gazi, Asim H, Zia, Jonathan, An, Sungtae, Lin, David J, Inan, Omer T, Kamaleswaran, Rishikesan
DOI: 10.1093/jamia/ocad067