Crowdbreaks tracks health trends using social media and crowdsourcing.
problem Challenges in tracking health trends using social media data.
method Open platform for continuous crowdsourced labelling of social media content.
result Accelerates research process in public health.
This paper analyzes air pollution trends in Rwanda using low-cost sensors and machine learning.
problem Lack of reliable air pollution data in Rwanda due to high costs of equipment.
method Analysis of existing data and development of forecasting models using low-cost sensors and machine learning.
result Proposes forecasting models for air pollution data collected by low-cost sensors.
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
problem Evaluating the predictive value of macroeconomic indicators for public health targets.
method Examined multiple forecasting approaches including neural networks, generalized additive models, random forests, and time series models with exogenous indicators.
result Macroeconomic indicators provide consistent and reproducible predictive signals for health workforce and infrastructure measures, but less so for other targets.
Study detects emerging trends in financial news articles about Microsoft.
problem Challenges in identifying trends in long-form financial news articles.
method Topic modeling and term frequency for keyword similarity analysis.
result Demonstrates the influence of the pandemic on Microsoft.
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining …
Personalized healthcare predictions using deep mixed effect model with Gaussian Processes.
problem Making personalized and reliable predictions from time-series data in healthcare.
method A composite model combining a deep neural network for global trends and Gaussian Processes for individual variability.
result Practical advantages over standard time-series deep models, demonstrated on diverse EHR datasets.
Detects anomalies in product health metrics at eBay for better alerts.
problem Detecting anomalies in unsupervised product health metrics at eBay.
method Developed a Moving Metric Detector (MMD) for anomaly detection and a point-wise ranking model for alert retrieval.
result Improves alert precision and avoids alert spamming in eBay production.
AI improves precision health through adaptive interventions.
problem Improving healthcare through personalized and dynamic treatments.
method Reinforcement learning (RL) for adaptive interventions in digital health.
result RL shows promise in dynamic healthcare problems.
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which med…
Paper proposes a framework to analyze DV on social media.
problem Lack of actionable knowledge from DV social media data.
method Develops a novel framework to model and discover themes related to DV.
result Provides actionable knowledge from DV social media content.
Deep learning improves skin cancer detection.
problem Early detection of skin cancer is challenging due to lack of professionals and instruments.
method Overview of recent deep learning models applied to skin cancer detection.
result Deep learning models enhance skin cancer detection accuracy.
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistica…
mcanalysis quantifies menstrual cycle effects in health data.
problem Lack of standardised statistical methods for menstrual cycle research.
method Fourier-basis generalised additive model (GAM) pipeline.
result Nine out of 15 health outcomes showed significant association with menstrual cycle.
In this paper, we propose an innovative investment framework incorporating asset allocation and class diversification oriented specifically for the biotechnology industry. With growing interests and capitalization in multiple biotech markets, investors require a more dynamic method of managing their assets within indiv…
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
problem Identifying financial health and reporting consistency in Kenyan medical insurance companies.
method Advanced clustering techniques (KMeans, DTW) on financial ratios and time series data.
result Four distinct clusters identified, each representing different financial performance and reporting consistency combinations.
Research predicts healthcare index movements using historical OHLC data.
problem Predicting the directional movement of healthcare indices based on historical data.
method Supervised classification task with a one-step-ahead rolling window, using a diverse feature set including OHLC ratios.
result Robust predictive performance with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6, highlighting the importance of nowcasting features.
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} structure in a {known,} fixed dictionary. We consider signals generated according to a natural probabilistic model, and establish new conditions…
MMformer improves forecasting of environmental time series data.
problem Accurately forecasting environmental change trends for policy-making.
method Meta-learning MTS model combining self-attention and adaptive transferable multi-head attention.
result MMformer outperforms other models in air quality and climate datasets, reducing prediction errors by 50% in MSE and 20% in MAE.
TMLE improves unbiased estimation in public health studies.
problem Improving unbiased estimation in observational studies.
method Targeted Maximum Likelihood Estimation (TMLE) integrates machine learning and statistical theory.
result TMLE has been adopted by researchers worldwide, especially outside the US.
Model predicts short-term Amazon rainforest fires with high accuracy.
problem Accurate short-term forecasting of Amazon rainforest fires is challenging.
method Used Seasonal and Trend decomposition based on Loess combined with multi-month-ahead load forecasting algorithms.
result Proposed decomposition-ensemble models provide more accurate forecasts than other models.
This paper proposes a multi-head attention model for predicting RUL in IIoT environments.
problem Estimating RUL for complex industrial equipment using IIoT data.
method Multi-Head Attention Mechanism combined with LSTM for multi-dimensional time-series data.
result The proposed model outperforms state-of-the-art models on benchmark datasets.
Systematic review of multimodal data challenges and solutions.
problem Challenges in integrating diverse data types for improved diagnostics and personalized care.
method Synthesizing findings from 69 studies on technical obstacles and recent methodological advances.
result Promising solutions like transfer learning, generative models, attention mechanisms, and neural architecture search.
Big data analytics improves healthcare through early detection and quality life.
problem Limited access to healthcare data hinders evidence-based decision-making.
method Analysis of healthcare data using various tools and techniques.
result Big data analytics enhances healthcare quality and patient outcomes.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
This paper compares ML models for predicting COVID-19 trends.
problem Forecasting the spread of COVID-19 to reduce its impact.
method Applied ARDL method to identify relationships, then used ML models (SVM, RF, KNN, ANN) for prediction.
result Models accurately forecasted COVID-19 cases with low MAPE.
Research uses Twitter to study transgender health issues.
problem Lack of information on transgender health needs.
method Collect and analyze tweets from transgender users.
result Identified 54 health topics, 7 categories, linguistic and topical differences between TM and TW.
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.
Develops a new trend power indicator using DSP techniques.
problem Determining the strength and reversibility of trends.
method Derives a novel indicator using digital signal processing.
result Accuracy of the new indicator correlates with PNL performance.
Repository tackles fake health news in cancer research.
problem Spread of fake health news over the internet.
method Developed comprehensive FakeHealth repository with rich features and detailed explanations.
result Repository helps in understanding and validating health fake news datasets.
System identifies health risks using semantic and machine learning.
problem Identifying risk factors associated with health conditions in subpopulations.
method Developed a combined semantic and machine learning system using a health risk ontology and knowledge graph.
result Dynamic discovery of risk factors and their subpopulations.
Study uses machine learning to predict future health from various health data types.
problem Predicting future health using diverse health data types.
method Applied machine learning (neural networks and XGBoost) to longitudinal data from 6830 individuals.
result Health-related measures were the strongest predictors of future health status, while genetic data performed poorly.
Study user engagement in mobile health apps for health workers in resource-poor settings.
problem Detect churn and tailor content for health workers in mobile health apps.
method Probabilistic and survival analysis of behavioral logs.
result Personalized measures of meaningful engagement can enhance health outcomes.
Forecast future volatilities and correlations based on current trends.
problem Predict future volatilities and correlations in financial markets.
method Use cubic and quadratic polynomials of current trend strengths.
result Accurate quantification of trend effects on volatilities and correlations.
Explains the difference between EMA and moving EMA, focusing on market trend indicators.
problem Understanding the difference between exponential moving average and moving exponential average.
method Explains the mathematical tools and definitions of trend indicators.
result Discusses the properties of the MACD indicator and its use in market trend analysis.
New algorithm forecasts health indicators for better equipment lifespan prediction.
problem Improving equipment lifespan prediction through health indicator forecasting.
method Generative + scenario matching approach using Gaussian Process.
result Superior performance compared to existing methods.
Bitcoin's attention is linked to Google Trends data, not general uncertainty.
problem Bitcoin's correlation with Google Trends data was previously misunderstood.
method Analyzed bidirectional relationships between Bitcoin returns and Google Trends attention over six days.
result Information flows from Bitcoin volatility to Google Trends attention, not the other way.
New fair regression methods improve health care spending predictions for undercompensated groups.
problem Current risk adjustment formulas underpredict spending for specific health groups, leading to unfair compensation.
method Developed new fair regression methods by integrating fairness considerations into the objective function.
result New methods lead to significant improvements in fairness (98%) with minimal impact on overall fit (4%).
RobustTrend filters time series trends robustly against outliers and abrupt changes.
problem Extracting accurate trend signals from noisy, potentially abrupt-changing time series.
method Uses Huber loss for outlier suppression and a combination of first and second order differences for regularization.
result Our algorithm outperforms existing methods in synthetic and real-world datasets.
LHIEM model predicts health, income, and employment over years.
problem Lack of path dependency in health policy simulations.
method Discrete-time microsimulation with Markov chain modules.
result Validates health care financing proposal through detailed modeling.
Study shows RNNs are effective for trend detection in time series.
problem Detecting trends in noisy time series data.
method Empirical investigation of standard RNNs for trend detection using simulated data.
result Standard RNNs structures outperform other estimators in trend detection.
Enhanced trend-following strategy using network momentum for commodity futures.
problem Improving systematic trend-following in commodity futures markets.
method Combines univariate and cross-sectional trend indicators, including network momentum.
result Statistically significant improvements in portfolio performance metrics.
Automatically assesses the quality of online health articles.
problem Lack of automated tools to evaluate the quality of online health information.
method Data mining approach using 10 quality criteria and feature selection.
result Classifier achieved 84%-90% accuracy on 10 criteria.
Paper develops a method for valid inference using language model predictions from verbal autopsy narratives.
problem Valid inference from verbal autopsy narratives for public health decision-making.
method Develops multiPPI++ method for valid inference using NLP techniques for COD prediction.
result Demonstrates the effectiveness of multiPPI++ in handling transportability issues and recovering ground truth estimates.
DynGraph2Seq predicts health stages from user activity graphs in online forums.
problem Predicting health stages from changing user activities in online forums.
method Formulated user activities as dynamic graphs, used DynGraph2Seq model with hierarchical attention.
result Demonstrated effectiveness and interpretability of DynGraph2Seq.
Early detection and precise characterization of emerging topics in text streams can be highly useful in applications such as timely and targeted public health interventions and discovering evolving regional business trends. Many methods have been proposed for detecting emerging events in text streams using topic modeli…
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk+ methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may…
This paper uses Bayesian models to analyze CTA returns across short and long-term trends.
problem The relative merits and interactions of short- and long-term trend systems in CTA replication remain controversial.
method Dynamic decomposition of CTA returns into short-term trend, long-term trend, and market beta factors using a Bayesian graphical model.
result The blend of horizons shapes the strategy's risk-adjusted performance.