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Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure
Open accessAbstract
Objective: When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients. Materials and Methods: Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort. Results: The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77–0.79), image model AUROC was 0.74 (0.72–0.75), and EHR model AUROC was 0.74 (0.70–0.76). For heart failure, combined: 0.83 (0.77–0.84), image: 0.80 (0.71–0.81), and EHR: 0.79 (0.75–0.82). For COPD, combined: AUROC = 0.88 (0.83–0.91), image: 0.83 (0.77–0.89), and EHR: 0.80 (0.76–0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia. Conclusions: Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.
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Calibration metrics are crucial for assessing model reliability.
“which are crucial for assessing model reliability [38,63,113,120,121].”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [63]
Only 8 studies performed independent external validation, revealing an average 3.5% decrease in AUC on external datasets.
“In this review, only 8 studies performed independent external validation [38,41,42,46,63,93,113,114], revealing an average 3.5% decrease in AUC on external datasets.”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [63]
Only 5 studies reported calibration metrics.
“In addition, only 5 studies reported calibration metrics, which are crucial for assessing model reliability [38,63,113,120,121].”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [63]
The combination of imaging and EHR data outperforms each of these modalities alone to differentiate between common causes of acute respiratory failure, such as heart failure, pneumonia or chronic obstructive pulmonary disease.
“and to differentiate between common causes of acute respiratory failure, such as heart failure, pneumonia or chronic obstructive pulmonary disease 100”
From Multimodal biomedical AI (2022) · cites this paper as [100]
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Metadata
- Authors
- Jabbour, S., Fouhey, D., Kazerooni, E., Wiens, J., & Sjoding, M. W.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Sjoding, Michael W
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-03-10
- Volume
- 29
- Issue
- 6
- Pages
- 1060-1068
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC9093032
- OpenAlex
- W4213439844
- Cited by
- 39
- References
- 30
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 11 · 2023: 6 · 2022: 5
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (K01HL136687, R01 HL158626, R01 LM013325)
- University of Michigan Precision Health Award
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- acute respiratory failure, chest radiograph, electronic health records, pneumonia, heart failure, chronic obstructive pulmonary disease, multimodal diagnosis, clinical decision support, external validation
- MeSH terms
- Machine Learning, Heart Failure, Humans, Respiratory Insufficiency, X-Rays, Pulmonary Disease, Chronic Obstructive, Electronic Health Records
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