Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. (10th March 2022)

• Record Type: Journal Article
• Authors: Jabbour, Sarah; Fouhey, David; Kazerooni, Ella; Wiens, Jenna; Sjoding, Michael W
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 6(2022)
• Page Start: 1060
• Page End: 1068
• Publication Date: 2022-03-10
• Subjects: machine learning -- acute respiratory failure -- chest X-ray -- electronic health record; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac030
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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.