| Autor: |
Tariq A; Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA. amara.tariq2@emory.edu., Celi LA; Massachusetts Institute of Technology, Boston, MA, USA.; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Newsome JM; Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, USA., Purkayastha S; School of Informatics Computing, Indiana University Purdue University, Indianapolis, IN, USA., Bhatia NK; Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA., Trivedi H; Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.; Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, USA., Gichoya JW; Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, USA., Banerjee I; Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.; Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, USA. |
| Abstrakt: |
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. |
