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During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

2D binary transition‐metal chalcogenides (TMCs) such as molybdenum disulfide exhibit excellent properties required for energy conversion applications. Alloying binary TMCs can form 2D compositionally complex TMC alloys (CCTMCAs) that possess remarkable properties from the constituent TMCs. High‐throughput workflow performing density functional theory (DFT) calculations based on the virtual crystal approximation (VCA) model (VCA‐DFT) is designed. The workflow is tested by predicting properties including in‐plane lattice constants, band gaps, effective masses, spin–orbit coupling, and band alignments of the Mo‐W‐S‐Se, Mo‐W‐S‐Te, and Mo‐W‐Se‐Te 2D CCTMCAs. The VCA‐DFT results are validated by computing the same properties using unit cells and supercells of selected compositions. The VCA‐DFT results of the abovementioned five properties are comparable to that of DFT calculations, with some inaccuracies in several properties of MoSTe and WSTe. Moreover, 2D CCTMCAs can form type II heterostructures as used in photovoltaics. Finally, Mo_0.5W_0.5SSe, Mo_0.5W_0.5STe, and Mo_0.5W_0.5SeTe 2D CCTMCAs are used to demonstrate the room‐temperature entropy‐stabilized alloys. They also exhibit high electrical conductivities at 300 K, promising for light adsorption devices. This work shows that the high‐throughput workflow using VCA‐DFT calculations provides a tradeoff between efficiency and accuracy, opening up opportunities in the computational design of other 2D CCTMCAs for various applications.