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Register a new userDeep Learning Models for Early Detection and Prediction of the spread of Novel Coronavirus (COVID-19)
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SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models were trained and tested on novel coronavirus 2019 dataset, which contains 19.53 Million confirmed cases of COVID-19. Our proposed models were evaluated by using Mean Absolute Error and compared with baseline method. Our experimental results, both quantitative and qualitative, demonstrate the superior prediction performance of the proposed models.
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Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.
COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hospital resources management and cross-infection control. We trained a deep feature fusion model to predict patient outcomes, where the model inputs were EHR data including demographic information, co-morbidities, vital signs and laboratory measurements, plus patients CXR images. The model output was patient outcomes defined as the most insensitive oxygen therapy required. For patients without CXR images, we employed Random Forest method for the prediction. Predictive risk scores for COVID-19 severe outcomes (CO-RISK score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance. The studys dataset (the MGB COVID Cohort) was constructed from all patients presenting to the Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020. ED visits with incomplete or erroneous data were excluded. Patients with no test order for COVID or confirmed negative test results were excluded. Patients under the age of 15 were also excluded. Finally, electronic health record (EHR) data from a total of 11060 COVID-19 confirmed or suspected patients were used in this study. Chest X-ray (CXR) images were also collected from each patient if available. Results show that CO-RISK score achieved area under the Curve (AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and 0.92 in 72 hours on the testing dataset. The model shows superior performance to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with physicians decisions, CO-RISK score has demonstrated superior performance to human in making ICU/floor decisions.
Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from condLSTM-Q inform people about the distribution of the predicted death tolls, allowing better evaluation of possible trajectories of the severity. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease, and could be further developed to generate other useful predictions such as new cases or hospitalizations intuitively.
The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put ones life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at S~ao Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.
Since the first coronavirus case was identified in the U.S. on Jan. 21, more than 1 million people in the U.S. have confirmed cases of COVID-19. This infectious respiratory disease has spread rapidly across more than 3000 counties and 50 states in the U.S. and have exhibited evolutionary clustering and complex triggering patterns. It is essential to understand the complex spacetime intertwined propagation of this disease so that accurate prediction or smart external intervention can be carried out. In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease. We further adopt a generative adversarial imitation learning framework to learn the model parameters. In comparison with the traditional likelihood-based learning methods, this imitation learning framework does not need to prespecify an intensity function, which alleviates the model-misspecification. Moreover, the adversarial learning procedure bypasses the difficult-to-evaluate integral involved in the likelihood evaluation, which makes the model inference more scalable with the data and variables. We showcase the dynamic learning performance on the COVID-19 confirmed cases in the U.S. and evaluate the social distancing policy based on the learned generative model.
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