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The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.

 

Purpose: Despite tremendous gains from deep learning and the promise of AI in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards like the TRIPOD, CONSORT, and CLAIM checklists is increasing to improve the peer review process and reporting of AI tools. However, no such standards exist for product level review. Methods: A review of the clinical trials shows a paucity of evidence for radiology AI products; thus, we developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. We applied the assessment tool to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools. Results: We find that there is limited technical information on methodologies for FDA approved algorithms compared to open source products, likely due to concerns of intellectual property. Furthermore, we find that FDA approved products use much smaller datasets compared to open-source AI tools, as the terms of use of public datasets are limited to academic and non-commercial entities which preclude their use in commercial products. Conclusion: Overall, we observe a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.