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.
Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.
problem Lack of a risk score to guide ECMO planning for critically ill patients.
method Leverages machine learning to develop the PEER score.
result PEER score predicts mortality and decompensation in patients eligible for ECMO.
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.
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).
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.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns. Most methods place limits on the use of viral load measurements, are often specific to a particular study design, and do not account for com…
New method infers viral load from pooled tests.
problem Inefficient viral load inference in pooled testing.
method Message passing algorithm with PCR noise function.
result Accurate viral load inference possible.
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.
Develops a method to analyze SARS-CoV-2 viral load vs. age, finding a significant increase.
problem Determining if SARS-CoV-2 viral load depends on patient age.
method Flexible, non-parametric, hierarchical, Bayesian, causal model.
result Statistically significant increase in viral load with age, but only for one dataset.
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.
The study introduces a high-dimensional tail index model for viral post analysis.
problem Empirical observation of power-law distributions in viral posts.
method High-dimensional tail index regression model, regularized estimator, debiasing for inference.
result Consistency and asymptotic normality of debiased estimator.
Study uses machine learning to detect early COVID-19 from CT images.
problem Early detection of COVID-19 from CT images.
method Machine learning methods applied to patches of CT images, feature extraction (GLCM, LDP, GLRLM, GLSZM, DWT), SVM classification.
result Best classification accuracy of 99.68% with 10-fold cross-validation and GLSZM feature extraction.
Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc. On the contrast, the large-scale network brings high requirements of both memory storage and computation resource,…
Deep learning ensembles improve COVID-19 detection from chest X-rays.
problem Detecting COVID-19 from chest X-rays using machine learning.
method Custom CNN and ImageNet models, transfer learning, iterative pruning, ensemble learning.
result 99.01% accuracy in detecting COVID-19 from chest X-rays.
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.
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images w…
Viral zoonoses have emerged as the key drivers of recent pandemics. Human infection by zoonotic viruses are either spillover events -- isolated infections that fail to cause a widespread contagion -- or species jumps, where successful adaptation to the new host leads to a pandemic. Despite expensive bio-surveillance ef…
Mathematician summarizes protein geometry and mutation effects.
problem Understanding how proteins mutate and their structure-function relationship.
method Mathematical analysis of protein structures and functions, focusing on hydrogen bonds and secondary structure.
result Protein secondary structure regulates mutation by stabilizing or destabilizing regions.
While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when …
Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties …
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
Paper tackles label noise in large datasets, purifying noisy data with a nonparametric framework.
problem Label noise in large-scale datasets with coarse labels.
method Develops a model-agnostic nonparametric framework for classification.
result Framework purifies noisy data using a small clean dataset and manages ambiguous samples.
Machine learning identifies potential drugs for COVID-19.
problem Finding effective treatments for COVID-19.
method Trained neural network models on virus protein sequences and antiviral drugs.
result Identified potential drug candidates for treating COVID-19.
Efficient AL method improves CNN performance with minimal labeled data.
problem Limited labeled data for training CNNs.
method Deep Evidential Active Learning (DEAL) using Dirichlet density.
result DEAL consistently outperforms other AL approaches and requires minimal computational resources.
A new framework maximizes influence spread in social networks by accounting for inter-community diffusion.
problem Real-world social networks have inter-community influence that is often overlooked in community-based IM approaches.
method Community-IM++ uses a heuristic based on community-based diffusion degree and progressive budgeting to model and prioritize cross-community diffusion.
result Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime than existing methods.
Paper studies SDL for better document and medical classification.
problem Imbalanced document classification and pneumonia detection.
method Developed novel convex and nonconvex algorithms for SDL.
result SDL improves classification accuracy when feature and label spaces differ.
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%.
The paper examines how sampling data affects the performance of submodular maximization.
problem Performance loss due to probability sampling in submodular maximization.
method Examines a simple probability sampling method where each data point is selected with probability at least r.
result The sampling gap is both upper and lower bounded by 1/r for policywise submodular utility functions.
The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions. However, the host (the owner of an online social platform) often faces more con…
New method targets vaccines for new variants using Thompson sampling.
problem Deciding how to prioritize vaccines for new variants with delayed feedback.
method Partial likelihood Thompson sampling, updating beliefs with partial likelihood.
result Method effectively prioritizes vaccines based on real-world data.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
problem Designing efficient drugs for novel viral proteins.
method End-to-end framework combining VAE, controlled sampling, and predictors.
result Highly selective and affinity molecules for SARS-CoV-2 targets.
This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedd…
This essay examines how what is considered to be artificial intelligence (AI) has changed over time and come to intersect with the expertise of the author. Initially, AI developed on a separate trajectory, both topically and institutionally, from pattern recognition, neural information processing, decision and control …
Develops a variational method for ultrametric phylogenetic trees.
problem Accurate and efficient approximation of posterior distributions over trees in Bayesian phylogenetics.
method Variational Bayesian approach based on coalescent times of a single-linkage clustering.
result Achieves competitive accuracy with significantly fewer gradient evaluations.
BoGA combines evolutionary search with Bayesian optimization for efficient protein design.
problem Designing novel proteins with specific characteristics is challenging due to sequence space complexity.
method BoGA integrates a genetic algorithm with Bayesian optimization to efficiently explore sequence space.
result BoGA accelerates discovery of high-confidence binders for diverse protein design objectives.
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.
Efficiently models agent dependencies in large social networks.
problem Challenges in incorporating inter-agent dependencies in social reinforcement learning models.
method Clusters users based on payoff and contribution, combines cluster-level policies with personalized agent-level policies, and uses dynamic clustering.
result Our approach learns more accurate policy estimates and converges more quickly than existing methods.
Noisy Pooled PCR tests large groups more efficiently.
problem Efficiently test large populations for viral infections.
method Converts group testing to a linear inverse problem with a message passing algorithm.
result Estimates patient illness status with fewer pooled measurements.
How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we lever…
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language…
Certain type of documents such as tweets are collected by specifying a set of keywords. As topics of interest change with time it is beneficial to adjust keywords dynamically. The challenge is that these need to be specified ahead of knowing the forthcoming documents and the underlying topics. The future topics should …
New method designs antimicrobial peptides with high potency and low toxicity.
problem Designing potent antimicrobial drugs with low toxicity.
method CLaSS method using deep generative autoencoder and atomistic simulations.
result Design and synthesis of two novel AMPs with high potency and low toxicity.
In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …
Framework predicts mortality risk in MAFLD subjects.
problem Lack of mortality prediction methods for MAFLD subjects.
method Artificial Intelligence-based framework MAFUS using ML algorithms.
result Support Vector Machines (SVM) is the best model for mortality prediction.
The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings. While deep learning (DL) models has become a promising predictive technology with near-hu…
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis i…
Paper uses deep learning to make predictions transparently.
problem Lack of transparency in DNN predictions.
method Extracts latent variables from trained DNNs for unified analysis.
result Improved prediction accuracy and transparency.