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Register a new userClassification supporting COVID-19 diagnostics based on patient survey data
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Distinguishing COVID-19 from other flu-like illnesses can be difficult due to ambiguous symptoms and still an initial experience of doctors. Whereas, it is crucial to filter out those sick patients who do not need to be tested for SARS-CoV-2 infection, especially in the event of the overwhelming increase in disease. As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated. Each of the methods was tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models was presented. The explanation enables the users to understand what was the basis of the decision made by the model. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set consisting of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
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The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-temporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities.
The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as the transformer model, to capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model fully utilizes publicly available information of COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level prediction as an aggregation of the corresponding county-level predictions. Our numerical experiments demonstrate that our model achieves the state-of-the-art performance among the publicly available benchmark models.
Audio classification using breath and cough samples has recently emerged as a low-cost, non-invasive, and accessible COVID-19 screening method. However, no application has been approved for official use at the time of writing due to the stringent reliability and accuracy requirements of the critical healthcare setting. To support the development of the Machine Learning classification models, we performed an extensive comparative investigation and ranking of 15 audio features, including less well-known ones. The results were verified on two independent COVID-19 sound datasets. By using the identified top-performing features, we have increased the COVID-19 classification accuracy by up to 17% on the Cambridge dataset, and up to 10% on the Coswara dataset, compared to the original baseline accuracy without our feature ranking.
Air pollution has a wide range of implications on agriculture, economy, road accidents, and health. In this paper, we use novel deep learning methods for short-term (multi-step-ahead) air-quality prediction in selected parts of Delhi, India. Our deep learning methods comprise of long short-term memory (LSTM) network models which also include some rece
Introduction: Real-world data generated from clinical practice can be used to analyze the real-world evidence (RWE) of COVID-19 pharmacotherapy and validate the results of randomized clinical trials (RCTs). Machine learning (ML) methods are being used in RWE and are promising tools for precision-medicine. In this study, ML methods are applied to study the efficacy of therapies on COVID-19 hospital admissions in the Valencian Region in Spain. Methods: 5244 and 1312 COVID-19 hospital admissions - dated between January 2020 and January 2021 from 10 health departments, were used respectively for training and validation of separate treatment-effect models (TE-ML) for remdesivir, corticosteroids, tocilizumab, lopinavir-ritonavir, azithromycin and chloroquine/hydroxychloroquine. 2390 admissions from 2 additional health departments were reserved as an independent test to analyze retrospectively the survival benefits of therapies in the population selected by the TE-ML models using cox-proportional hazard models. TE-ML models were adjusted using treatment propensity scores to control for pre-treatment confounding variables associated to outcome and further evaluated for futility. ML architecture was based on boosted decision-trees. Results: In the populations identified by the TE-ML models, only Remdesivir and Tocilizumab were significantly associated with an increase in survival time, with hazard ratios of 0.41 (P = 0.04) and 0.21 (P = 0.001), respectively. No survival benefits from chloroquine derivatives, lopinavir-ritonavir and azithromycin were demonstrated. Tools to explain the predictions of TE-ML models are explored at patient-level as potential tools for personalized decision making and precision medicine. Conclusion: ML methods are suitable tools toward RWE analysis of COVID-19 pharmacotherapies. Results obtained reproduce published results on RWE and validate the results from RCTs.
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