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Journal
Journal of the American Medical Informatics Association
The official journal of the American Medical Informatics Association. Publishes peer-reviewed research for biomedical and health informatics. Coverage includes clinical care, clinical research, translational science, policy, among other subjects.
Journal
JAMIA Open
Learn more about the newest journal of the American Medical Informatics Association, beginning publication in 2018.
Journal Article
External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older
Journal of the American Medical Informatics Association, ocaf062, https://doi.org/10.1093/jamia/ocaf062
Published: 29 April 2025
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All-cause mortality stratified by the End-of-Life Care Index. Figure dep...
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Journal of the American Medical Informatics Association
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External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older
Published: 29 April 2025
Figure 1.
All-cause mortality stratified by the End-of-Life Care Index. Figure depicting the increasing cumulative incidence of mortality with higher scores on the EOL-CI for low-,medium-, and high-risk adults.
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Incidence of outpatient encounters for advance care planning by Epic End-of...
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Journal of the American Medical Informatics Association
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External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older
Published: 29 April 2025
Figure 3.
Incidence of outpatient encounters for advance care planning by Epic End-of-Life Care Index Score. Analyses restricted to subgroup of older adults (N = 83 783) with a primary care provider within the Atrium Health-Wake Forest Baptist system. Figure depicting the incidence of outpatient encounters
Journal Article
Primary care staff members’ experiences with managing electronic health record inbox messages Get access
Journal of the American Medical Informatics Association, ocaf067, https://doi.org/10.1093/jamia/ocaf067
Published: 29 April 2025
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Distribution of the Epic End-of-Life Care Index Score, with and without loc...
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Journal of the American Medical Informatics Association
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External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older
Published: 29 April 2025
Figure 2.
Distribution of the Epic End-of-Life Care Index Score, with and without local modification, and resulting calibration with respect to observed 1-year mortality. (A) Calibration of Epic End-of-Life Index Score treating it as a probability (Initial), after re-calibration based on cross-validated Cox r
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The ML algorithm to predict the PGD in heart transplant. The PGD patient... Get access
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Journal of the American Medical Informatics Association
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Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning
Published: 28 April 2025
Figure 1.
The ML algorithm to predict the PGD in heart transplant. The PGD patient cohort is selected from UNOS database. Then input features will be selected with domain expert guidance. ML will be applied to predict PGD and feature importance will be analyzed.
Journal Article
Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning Get access
Journal of the American Medical Informatics Association, ocaf066, https://doi.org/10.1093/jamia/ocaf066
Published: 28 April 2025
Journal Article
Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning
in
JAMIA Open
JAMIA Open, Volume 8, Issue 2, April 2025, ooaf032, https://doi.org/10.1093/jamiaopen/ooaf032
Published: 26 April 2025
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(A) LSTM to estimate propensity score using claims records. Average pooling...
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JAMIA Open
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Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning
Published: 26 April 2025
Figure 2.
(A) LSTM to estimate propensity score using claims records. Average pooling is applied to the code representations to generate record representation, aggregating the representations of the codes present in the record. For example, if Record t contains Fever , Acetaminophen , and Co
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(A) Causal diagram of our problem setup. A denotes binary treatment a...
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JAMIA Open
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Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning
Published: 26 April 2025
Figure 1.
(A) Causal diagram of our problem setup. A denotes binary treatment and Y denotes continuous outcome. A claims record x t includes medical codes and also can include a treatment assignment A . (B) Causal diagram of a hypothetical confounding scenario in our experiment
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Visual representation of attention weights at the last encoder layer of ...
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JAMIA Open
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Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning
Published: 26 April 2025
Figure 3.
Visual representation of attention weights at the last encoder layer of BERT code for selected samples from the test set. C indicates the position of confounding variables. [CLS] indicates the position of [CLS] token. Darker color represents higher attention weight. (A). Synthetic dat
Journal Article
“Everything is electronic health record-driven”: the role of the electronic health record in the emergency department diagnostic process
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JAMIA Open
JAMIA Open, Volume 8, Issue 2, April 2025, ooaf029, https://doi.org/10.1093/jamiaopen/ooaf029
Published: 23 April 2025
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Alignment of identified themes related to EHR use in the ED diagnostic care...
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JAMIA Open
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“Everything is electronic health record-driven”: the role of the electronic health record in the emergency department diagnostic process
Published: 23 April 2025
Figure 1.
Alignment of identified themes related to EHR use in the ED diagnostic care process on the ED-adapted NASEM diagnostic safety framework. This figure aligns the themes identified in this study with the a conceptual framework showing the process of ED care, from left to right: patient experiences &
Journal Article
A resource for Logical Observation Identifiers Names and Codes terms that may be associated with identifying information Get access
Journal of the American Medical Informatics Association, ocaf061, https://doi.org/10.1093/jamia/ocaf061
Published: 22 April 2025
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Trust-MAPS augment the “corrected data” computed using projections onto phy... Get access
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Journal of the American Medical Informatics Association
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Improving clinical decision support through interpretable machine learning and error handling in electronic health records
Published: 22 April 2025
Figure 2.
Trust-MAPS augment the “corrected data” computed using projections onto physical constraints P , with “trust-scores” which capture the distance of the corrected data from homeostasis constraints. This augmented data are then used in downstream ML for predictions, which significantly improves the pe
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Example Logical Observation Identifiers, Names, and Codes terms with and wi... Get access
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Journal of the American Medical Informatics Association
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A resource for Logical Observation Identifiers Names and Codes terms that may be associated with identifying information
Published: 22 April 2025
Figure 1.
Example Logical Observation Identifiers, Names, and Codes terms with and without a method part. Abbreviations: Pt, point (to identify measures at a point in time); Qn, quantitative; SCnc, substance concentration; Sub, substance amount. This figure describes two different LOINC terms for sodium in
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A visualization of the Trust-MAPS processing on clinical data. In the first... Get access
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Journal of the American Medical Informatics Association
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Improving clinical decision support through interpretable machine learning and error handling in electronic health records
Published: 22 April 2025
Figure 1.
A visualization of the Trust-MAPS processing on clinical data. In the first step, outlier data points that lie outside the physically possible constraints are projected to lie within the constraints. This is to prevent the machine learning model from learning incorrect patterns from erroneous data a
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Left : A plot comparing receiver-operating characteristic curves for sepsis... Get access
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Journal of the American Medical Informatics Association
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Improving clinical decision support through interpretable machine learning and error handling in electronic health records
Published: 22 April 2025
Figure 4.
Left : A plot comparing receiver-operating characteristic curves for sepsis prediction machine learning model on the dataset processing with each step of the Trust-MAPS process, and baseline models trained without Trust-MAPS. Right : A plot comparing precision-recall curves for sepsis prediction ma
