The study uses demographical data to predict health conditions.
problem Predicting health conditions based on patient demographics and symptoms.
method Analyzing Electronic Health Records (EHR) from Brazil to identify age-related clusters of health conditions.
result Age of patients significantly influences the likelihood of certain health conditions.
Mobile health data predicts chronic conditions better than daily activity.
problem Improving chronic condition prediction accuracy.
method Intra-day step and sleep data from mobile health devices, Convolutional Neural Networks, multi-task learning.
result Intra-day activity data significantly improves chronic condition classification.
The paper develops methods for monitoring TPL machine health.
problem Inaccurate and untimely maintenance of TPL systems leads to poor quality and inefficiencies.
method Physics-informed data-driven predictive models integrated with statistical approaches.
result The methods achieve high accuracy across various scenarios and conditions.
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.
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.
Automated process links oral health to systemic conditions using machine learning.
problem Correlating oral health with systemic health conditions.
method Intraoral fluorescent biomarker imaging, machine learning segmentation, and clinical examination.
result Machine learning classifier achieved AUC of 0.677, indicating a learned association between disease signatures in images and periodontal disease.
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 integrates diverse data sources to predict mental health conditions.
problem Predict individuals' mental health conditions using a heterogeneous network approach.
method Leverage a heterogeneous information network (HIN) to model social interaction, health data, and survey data. Apply recommender system (RS) and node classification (NC) paradigms to predict mental health states.
result RS and NC methods outperform traditional logistic regression models in predicting mental health conditions.
A Longitudinal Attribute-Conditioned Neural Network (LANTERN) framework for modeling health-state transition probabilities in irregular longitudinal data.
problem Estimating long-term care transition probabilities in irregular longitudinal health data.
method A neural network that learns from individual health history, incorporates time elapsed, and conditions on demographic and socioeconomic attributes.
result Improves severe disability discrimination and maintains strong calibration.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
problem Impact of health care capacity on COVID-19 growth in Senegal.
method Compartmental model with logistic growth health care capacity, machine learning projection.
result Condition to avoid overwhelming health care system provided.
StageNet improves health risk prediction by integrating disease stage information.
problem Improving health risk prediction for patients with chronic conditions.
method StageNet uses a stage-aware LSTM and stage-adaptive convolutional modules to extract and integrate disease stage information.
result StageNet achieves up to 12% higher AUPRC for risk prediction and over 58% higher Calinski-Harabasz score for patient subtyping compared to state-of-the-art models.
Study assesses health plan risk measures for Solvency Capital Requirement.
problem Assessing risk measures for health plans to meet Solvency Capital Requirement.
method Three-part regression model with three GLMs for claim counts, episode allocation, and severity.
result Reduction in regression models compared to traditional methods.
Tensor analysis improves structural health monitoring of complex aerospace systems.
problem Highly redundant and correlated sensor data in structural health monitoring.
method Tensor-based learning for multi-way structural data analysis.
result Tensor-based approach successfully detects damage in aeroservoelastic models.
Bayesian approach predicts battery health under varied conditions.
problem Accurately predicting battery health for reliable operation and investment valuation.
method Gaussian process regression with Bayesian non-parametric feature selection.
result Method accurately predicts battery capacity fade with low error.
Develops a personalized reinforcement learning algorithm for dyadic health interventions.
problem Personalizing health interventions for dyadic relationships in mobile health.
method Dyadic Reinforcement Learning (dyadic RL), a Bayesian and hierarchical online algorithm.
result Established a regret bound and demonstrated empirical performance through simulations and real data.
Paper proposes a method to model health outcomes using varying-coefficients and KNN-based LASSO.
problem Modeling health outcomes like BMI and cholesterol levels with varying age effects.
method Varying-coefficients regional quantile regression via KNN fused LASSO, with ADMM algorithm.
result Efficacy in capturing complex age-dependent associations between health outcomes and risk factors.
AdaCare learns health status from biomarkers across multiple time scales.
problem Lack of explicit extraction of historical biomarker variation and adaptability to diverse patient conditions.
method Scale-adaptive feature extraction and recalibration for interpretability.
result AdaCare achieves state-of-the-art prediction accuracy and provides interpretable results.
Model predicts default risk based on company's financial forecasts and credit conditions.
problem Estimating the risk of a company defaulting on its financial obligations.
method Developed an equilibrium model linking interest rates to corporate performance and credit supply.
result Estimates idiosyncratic default risk and provides forward-looking probability of default (PD).
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
Unsupervised learning uncovers hidden patterns in health data.
problem Limited scalability and accuracy of supervised learning in identifying complex clinical patterns.
method Derives from Lasko et al. method, implemented in Apache Spark and Python, generalized for MIMIC-III lab data.
result Unsupervised learning finds patterns in EHRs that reveal subtypes of diseases.
The CHAMPION study clusters multi-dimensional accelerometer data to understand health links.
problem Clustering multi-dimensional data from pediatric longitudinal studies.
method Developed a finite mixture of multidimensional arrays model for clustering 4-dimensional accelerometer data.
result Demonstrated the feasibility and utility of clustering higher order data.
Paper proposes a hierarchical approach for early anomaly detection in time series data for critical health events.
problem Early detection of critical health events in intensive care units.
method A layered learning architecture that breaks the problem into pre-conditional and event layers.
result The proposed method outperforms state-of-the-art approaches for critical health episode prediction.
Machine learning faces challenges in healthcare data, but offers opportunities.
problem Poorly labeled data, multiple endotypes, and underrepresented healthy individuals.
method Review of existing machine learning methods and challenges in healthcare.
result Opportunities for machine learning in healthcare identified.
SURI boosts features with high unique relevant information for better health data analysis.
problem Preserving interpretability in health data analysis.
method Mutual information-based feature selection (MIBFS) method called SURI.
result SURI selects more relevant features leading to higher classification performance.
The paper develops a method to optimize individualized treatment rules for cost-effectiveness.
problem Developing cost-effective individualized treatment rules for healthcare policy.
method Using conditional random forest and net-monetary-benefit (NMB) to estimate optimal CE-ITR.
result The approach optimizes healthcare resource allocation by maximizing health gains and minimizing costs.
Review of automatic de-identification systems for EHR, highlighting challenges beyond accuracy.
problem Challenges in surrogate generation and patient privacy in de-identification of EHR.
method Comprehensive review of 18 recently published systems, focusing on accuracy and challenges.
result Despite accuracy improvements, challenges remain in surrogate generation and patient privacy.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.
Novel unsupervised scheme for highly imbalanced and overlapping datasets.
problem Highly imbalanced and overlapping classes in medical datasets.
method Unsupervised domain adaptation scheme based on Quantification.
result High quality results for Quantification and Domain Adaptation.
New method estimates personalized treatment plans for mobile health.
problem Personalized treatment plans for patients with type 1 diabetes.
method Reinforcement learning for minute-by-minute decision making.
result Proposed method consistent and asymptotically normal.
Generative model predicts vehicle faults up to 1000 hours in advance.
problem Forecasting vehicle faults for predictive maintenance.
method Generative model trained on US Army data, incorporating real-world factors.
result Highly accurate predictions of time to first fault.
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.
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.
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and gen…
Proposes a new model for predicting chronic conditions over time.
problem Predicting complex relationships between multiple chronic conditions.
method Continuous time Bayesian network with adaptive regularization for structure and parameter learning.
result Proposed model provides sparse, intuitive representation of chronic condition relationships.
Improves flu prediction by blending environment and population info.
problem Challenges in using data from one environment in another due to feature variability and population subgroup differences.
method Population-aware hierarchical Bayesian domain adaptation framework with multiple invariant components.
result Model improves flu prediction in new environments with unlabelled data.
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.
Generative models create personalized patient health simulations.
problem Creating accurate digital twins for personalized medicine.
method Neural network architecture for conditional generative models of clinical trajectories.
result Same architecture generates accurate twins across 13 indications.
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.
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%).
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.
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.
Two new estimators reduce costs and improve accuracy for EHR outcome prediction.
problem Sparse estimate distributions, high computational cost, and high sampling variance in EHR outcome prediction.
method Proposed SCOPE and REACH estimators that leverage next-token probability distributions.
result SCOPE and REACH match Monte Carlo accuracy with token reductions of 2.5-3.4 times and variance guarantees.
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.
The paper proposes a neural network model for estimating conditional mixture Weibull distributions with right-censored data.
problem Survival analysis with right-censored data in predictive maintenance and health fields.
method A neural network architecture is developed to estimate two-parameter Weibull distributions conditionally to features, extending to a finite mixture of Weibull distributions.
result The model outperforms state-of-the-art methods on real-world datasets and can consider any survival time horizon.
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.
High-throughput machine learning predicts thousands of diagnosis codes with high accuracy.
problem Predicting disease risk for thousands of diagnosis codes at various time points.
method Training machine learning algorithms on EHR data to predict diagnosis risks.
result Achieved AUCs of 0.803 and 0.758 for 1 and 6-month predictions, respectively.
Research uses Twitter data to identify health issues in gay users.
problem Lack of information on health issues of LGBTQ people.
method Collected and analyzed tweets from gay users on health topics.
result Identified 11 diseases in 7 categories.
Framework identifies comorbidities for frequent ED and inpatient visits.
problem Reducing resource usage and costs in frequent patients.
method Developed MSAR algorithm to identify comorbidities.
result MSAR identifies conditions most associated with reoccurring ED and inpatient visits.