Correction to: Privacy-protecting, reliable response data discovery using COVID-19 patient observations.
Author(s):
DOI: 10.1093/jamia/ocad069
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
Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept.
Author(s): Stead, William W, Lewis, Adam, Giuse, Nunzia B, Koonce, Taneya Y, Bastarache, Lisa
DOI: 10.1093/jamia/ocad078
To describe the application of nudges within electronic health records (EHRs) and their effects on inpatient care delivery, and identify design features that support effective decision-making without the use of interruptive alerts.
Author(s): Raban, Magdalena Z, Gates, Peter J, Gamboa, Sarah, Gonzalez, Gabriela, Westbrook, Johanna I
DOI: 10.1093/jamia/ocad083
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
Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID [...]
Author(s): Pfaff, Emily R, Girvin, Andrew T, Crosskey, Miles, Gangireddy, Srushti, Master, Hiral, Wei, Wei-Qi, Kerchberger, V Eric, Weiner, Mark, Harris, Paul A, Basford, Melissa, Lunt, Chris, Chute, Christopher G, Moffitt, Richard A, Haendel, Melissa, ,
DOI: 10.1093/jamia/ocad077
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad076
We propose a system, quEHRy, to retrieve precise, interpretable answers to natural language questions from structured data in electronic health records (EHRs).
Author(s): Soni, Sarvesh, Datta, Surabhi, Roberts, Kirk
DOI: 10.1093/jamia/ocad050
Screening for chronic kidney disease (CKD) requires an estimated glomerular filtration rate (eGFR, mL/min/1.73 m2) from a blood sample and a proteinuria level from a urinalysis. We developed machine-learning models to detect CKD without blood collection, predicting an eGFR less than 60 (eGFR60 model) or 45 (eGFR45 model) using a urine dipstick test.
Author(s): Jang, Eun Chan, Park, Young Min, Han, Hyun Wook, Lee, Christopher Seungkyu, Kang, Eun Seok, Lee, Yu Ho, Nam, Sang Min
DOI: 10.1093/jamia/ocad051
Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) [...]
Author(s): Parsons, Rex, Blythe, Robin, Cramb, Susanna M, McPhail, Steven M
DOI: 10.1093/jamia/ocad042