Let the data speak: analysing data from multiple health centers of the São Paulo metropolitan area for COVID-19 clinical deterioration prediction

With the spread of different COVID-19 variants in the Brazilian territory, the national health system has been facing a constant overload. Using data from five different health centers located in the Sao Paulo metropolitan area, this work seeks to identify key common factors associated with the prognosis of COVID-19 severity. The proxies for severity considered are hospitalization time, death and use of mechanical ventilation. The induced models predicted ob-jective short-term COVID-19 clinical deterioration outcomes with AUC, sensitivity and specificity up to 0.880, 0.824 and 0.833, respectively. Parameters such as C-reactive protein and percentage of neutrophils have shown most influence on the predictions. Given the nature of the lab tests highlighted, we note that innate inflammatory status in admission can play a significant role in patient outcome. © 2022 IEEE.

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans.

The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.