A new COVID-19 CT dataset helps develop AI diagnosis models.
problem Lack of publicly available COVID-19 CT datasets due to privacy issues.
method Built an open-sourced COVID-CT dataset and developed AI diagnosis methods.
result Developed AI diagnosis models achieving high accuracy and performance.
Deep learning for COVID-19 diagnosis using CXR images with limited data.
problem Difficulty in collecting CXR data for deep learning due to the pandemic.
method Patch-based convolutional neural network with limited trainable parameters.
result Achieves state-of-the-art performance and interpretable saliency maps.
Machine learning model diagnoses COVID-19 from routine blood tests.
problem Difficulty in diagnosing COVID-19 due to inconsistent blood parameter changes.
method Constructed a machine learning model using 5,333 patients with various infections and 160 COVID-19-positive patients.
result Cross-validated AUC of 0.97, sensitivity of 81.9%, specificity of 97.9%.
AI4COVID-19 app diagnoses COVID-19 from cough samples.
problem Scalable screening tool for COVID-19 testing.
method Transfer learning and multi-pronged AI architecture.
result AI4COVID-19 can distinguish COVID-19 coughs from others.
DeTraC CNN detects COVID-19 from chest X-rays with high accuracy.
problem Limited availability of annotated medical images for COVID-19 diagnosis.
method Transfer learning and class decomposition mechanism in DeTraC CNN.
result 95.12% accuracy in detecting COVID-19 from normal and severe cases.
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
problem Manual, time-consuming pneumonia diagnosis with high inter-observer variability.
method Multi-level ensemble classification system using Bayesian Deep Learning.
result Accuracy of 98.06% in differentiating four pathologies.
Bayesian CNN estimates uncertainty in COVID-19 detection.
problem Uncertainty in deep learning predictions for medical diagnosis.
method Drop-weight based Bayesian Convolutional Neural Networks (BCNN).
result Uncertainty correlates with prediction accuracy.
CoroNet detects COVID-19 from chest X-rays with high accuracy.
problem Detecting COVID-19 from chest X-rays using limited testing kits.
method Proposes CoroNet, a deep neural network based on Xception architecture trained on a combined dataset of COVID-19 and pneumonia X-rays.
result CoroNet achieved an overall accuracy of 89.6% and precision/recall rates of 93%/98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal).
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.
problem Learning new concepts without examples, especially in medical imaging.
method Uses existing knowledge and auxiliary information to predict unknown concepts.
result Effective in diagnosing COVID-19 from chest X-rays.
Deep CNNs diagnose chest X-rays for COVID-19 and other pneumonia.
problem Diagnosing pneumonia from chest X-rays.
method Training and testing VGG16, VGG19, InceptionResNetV2, InceptionV3, and Xception networks on chest X-rays.
result VGG16 with dropout achieves high accuracy and sensitivity.
This study interprets machine learning models to identify biomarkers for severe COVID-19 infection.
problem The black-box nature of machine learning models makes it difficult for medical researchers to understand and trust their predictions.
method The study uses permutation feature importance, Partial Dependence Plot, Individual Conditional Expectation, Accumulated Local Effects, Local Interpretable Model-agnostic Explanations, and Shapley Additive Explanation to interpret four machine learning models.
result The study identifies NTproBNP, CRP, LDH, LYM, leukocytes, eosinophils, and platelets as biomarkers associated with severe COVID-19 infection.
Ensemble classifier detects pneumonia patterns in chest CT images.
problem Manual and time-consuming diagnosis of pneumonia in chest CT images.
method Probabilistic Support Vector Machine (SVM) ensemble, kernel PCA, patch-based classification.
result 97.86% accuracy in pneumonia detection.
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
problem Developing reliable machine learning models for COVID-19 diagnosis and prognosis from chest images.
method Systematic review of machine learning models published in 2020.
result None of the models identified are of clinical use due to methodological flaws and biases.
Proposes a sample-efficient method for uncertainty estimation in deep learning.
problem Inaccurate uncertainty estimation in deep learning models, especially with limited data.
method Probabilistic Neighbourhood Component Analysis (PCA) for sample-efficient uncertainty estimation.
result Demonstrates superior uncertainty quantification compared to state-of-the-art methods.
Study identifies COVID-19 pneumonia from chest X-rays.
problem Identifying COVID-19 pneumonia from other types and healthy lungs using CXR images.
method Proposed a multi-class and hierarchical classification schema using CXR images, texture descriptors, and a pre-trained CNN model. Employed resampling algorithms and early/late fusion techniques.
result Achieved macro-avg F1-Score of 0.65 and F1-Score of 0.89 for COVID-19 identification in hierarchical classification scenario.
New framework optimizes label shift adaptation using aligned distribution mixture.
problem Label shift where source and target label distributions differ.
method Aligned Distribution Mixture (ADM) framework, incorporating insights from generalization theory.
result The ADM framework improves four typical label shift methods and introduces a one-step approach.
This paper proposes a new RWO-Sampling (Random Walk Over-Sampling) based on graphs for imbalanced datasets. In this method, two schemes based on under-sampling and over-sampling methods are introduced to keep the proximity information robust to noises and outliers. After constructing the first graph on minority class, …
Examines US equity risk premiums amid COVID-19.
problem Analyzing equity risk premiums during the pandemic.
method Not specified in the abstract.
result Not specified in the abstract.
Developed a neural topic model for classifying COVID-19 disinformation.
problem Tackles the challenge of disinformation during the COVID-19 pandemic.
method Classification-aware neural topic model (CANTM) for COVID-19 disinformation.
result Demonstrated the effectiveness of CANTM in classifying COVID-19 disinformation.
Researchers use clustering to differentiate COVID-19 lung scans.
problem Identifying infected individuals with COVID-19.
method Applied unsupervised clustering techniques using PCA, K-Means++, and RCC.
result KM++ and RCC algorithms improved in clustering COVID-19 lung scans.
Study quantifies how COVID-19 spread affects US stock markets.
problem Impact of COVID-19 on US stock market during pandemic.
method Developed a novel temporal complex network approach using econometric and ML models.
result Local spread of COVID-19 and Google searches impact abnormal stock prices.
Study shows tweets about COVID-19 can predict stock market performance.
problem Understanding the impact of COVID-19 on stock markets.
method Text sentiment analysis of Twitter data to correlate tweets about COVID-19 with stock market performance.
result Strong relationship between COVID-19 sentiment and stock market performance can be predicted.
Paper examines global Covid-19 data complexity and finds low intrinsic dimensions.
problem Understanding the complexity of Covid-19 data across countries.
method Used a Bayesian mixture model (Hidalgo) to estimate intrinsic dimensionality.
result Covid-19 data projects onto two low-dimensional manifolds without significant loss of information.
A new model characterizes undocumented and asymptomatic infections to quantify COVID-19 uncertainties.
problem Quantifying uncertainties in COVID-19 infections and contagion.
method SUDR model: characterizes undocumented and documented infections, captures probabilistic density, and incorporates Bayesian inference.
result Demonstrates deeper understanding of COVID-19 uncertainties compared to classic models.
Study examines short-term stress of COVID-19 on major global stock indices.
problem Short-term impact of COVID-19 on global stock markets.
method Secondary data from 41 stock exchanges in 32 countries, focusing on first reported cases.
result Volatility in stock markets increases with the rise of COVID-19 cases, and there is a significant negative correlation.
Machine learning models detect COVID-19 from routine blood tests.
problem Separating COVID-19 from other viral pneumonias using blood tests.
method Employed random forests and support vector machines on blood data.
result SVM-based classifier achieves 84% accuracy in detecting COVID-19.
Study uses machine learning to analyze Twitter sentiments about COVID-19.
problem Examining public concerns and sentiments about COVID-19 from Twitter.
method Machine learning (Latent Dirichlet Allocation) to identify topics and sentiments.
result Identified 13 topics and categorized into five themes, revealing dominant fears and mixed feelings.
Deep learning models predict COVID-19 spread.
problem Predicting the spread of COVID-19 to mitigate its impact.
method Proposed DSPM and NRM models trained on 19.53M cases.
result Superior prediction performance of proposed models.
Study repurposes open data to find potential COVID-19 drugs.
problem Developing effective treatments for COVID-19.
method Deep learning network-based approach using large scientific corpus.
result Identified 41 repurposable drugs for COVID-19.
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
problem Developing tools to monitor high-risk patients during the COVID-19 pandemic.
method Data-driven random forest classification model using baseline characteristics and symptoms.
result Model predicts COVID-19 mortality with excellent performance (AUC: 0.91), identifying novel predictors.
Mathematical models help keep vaccine prices low.
problem Pricing COVID-19 vaccines to ensure affordability and profitability.
method Optimization and game theory approaches modeling a duopoly market.
result Government can negotiate low prices while manufacturers earn profits.
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
problem Impact of COVID-19 on stock and oil prices forecasting.
method Integrates Stationary Wavelet Transform and Bidirectional Long Short-Term Memory networks.
result BDLSTM+WT-ADA achieved satisfactory results in Crude Oil price forecasting.
Develops Lagrange-Hamilton geometry for COVID-19 disease dynamics.
problem Modeling the spread of COVID-19 disease.
method Least squares variational method, nonlinear connections, d-torsions, Lagrangian Yang-Mills.
result Jacobi stability of the dynamical system.
Deep IDA integrates multi-view data to classify COVID-19 severity, identifying molecular signatures.
problem Understanding the complexity of COVID-19 severity from multi-view clinical and molecular data.
method Deep IDA learns nonlinear projections to maximize view associations and class separations, with feature ranking.
result Deep IDA outperforms other methods in classifying COVID-19 severity and identifies interpretable molecular signatures.
Study uses social media to analyze COVID-19 impact.
problem Understanding the global impact of COVID-19.
method Machine learning and linguistic tools to analyze social media posts.
result Automatic detection of positive reports of COVID-19.
Machine learning predicts COVID-19 activity in China.
problem Real-time forecasting of COVID-19 activity in Chinese provinces.
method Combines mechanistic disease models with digital traces (internet searches, news alerts). Uses clustering and data augmentation techniques.
result Stable and accurate forecasts 2 days ahead of current time, outperforming baseline models in 27 out of 32 provinces.
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
problem Impact of COVID-19 on consumer behavior in the restaurant industry.
method Bayesian regression with Hamiltonian Monte Carlo.
result Estimates change in consumer behavior before and after the pandemic.
Study nationwide measures' impact on COVID-19 using models and machine learning.
problem Analyzing the impact of nationwide COVID-19 measures.
method Compartmental model and machine learning tools.
result Comparison of deterministic model and machine learning forecasts.
The study uses stock market indicators to forecast COVID-19 cases.
problem Forecasting the spread of COVID-19 for resource allocation.
method Reinterpreting daily cases as candlesticks and applying stock market indicators.
result The stock market indicators show statistical significance in predicting COVID-19 cases.
Paper analyzes factors affecting COVID-19 risk in US counties.
problem Identifying factors influencing COVID-19 risk in US counties.
method Combines unsupervised (K-means clustering) and supervised learning models.
result Mean temperature, poverty, obesity, and other factors are most significant.
Study examines how COVID-19 affects bond yields using network filtering methods.
problem Impact of COVID-19 on sovereign bond yields.
method Network filtering methods applied to a correlation matrix of sovereign bond yields.
result Mean correlation decreases across all filtering methods during the COVID-19 period.
AICov integrates population covariates for better COVID-19 forecasting.
problem Forecasting COVID-19 with broader social context.
method Integrative deep learning framework with LSTM and multiple data sources.
result Improved prediction of COVID-19 cases and deaths with population risk factors.
Neural networks model COVID-19 spread with partial isolation data.
problem Modeling the spread of COVID-19 with limited data.
method Semi-supervised neural networks solving inverse problems.
result Estimates optimal conditions for virus spread and passive population.
Deep learning identifies transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity.
problem Understanding how SARS-CoV-2 varies in infecting and causing severe COVID-19.
method Developed a new approach to generating self-supervised edge features, using Graph Attention Networks (GAT) and Set Transformer.
result Achieved state-of-the-art performance in predicting disease state of individual cells using single-cell RNA sequencing data.
Study shows COVID-19 increases stock market crash risk in China.
problem Impact of COVID-19 on stock market crash risk in China.
method Estimated conditional skewness using GARCH-S model and constructed fear index from Baidu Index data.
result Conditional skewness reacts negatively to daily growth in total confirmed cases, indicating increased crash risk.
AI model predicts stock prices using social media data and hybrid neural networks.
problem Predicting stock price movements during the COVID-19 pandemic.
method Integrates social media trends and historical stock data using a hybrid CNN-BLSTM framework.
result The proposed framework outperforms traditional models in predicting stock price movements.
The abstract discusses open data resources for studying and controlling the spread of COVID-19.
problem Understanding and controlling the spread of COVID-19.
method Identification and description of open data resources and data-driven methodologies.
result Identification of variables and open data resources for analyzing COVID-19.
DeCom predicts post-COVID RSV timing and intensity with NPI consideration.
problem Predicting RSV timing and intensity post-COVID with NPI impact.
method Deep coupled tensor factorization machine (DeCom) leveraging tensor factorization and residual modeling.
result DeCom achieves up to 46% lower RMSE and 49% lower MAE compared to baselines.