Deep learning system tracks stool consistency for GI disease assessment.
problem Lack of objective stool consistency measurements in chronic GI disease.
method Computer vision and deep convolutional neural networks (CNN).
result Developed a stool detection and tracking system.
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks, and the reproducible experimental results signify the efficiency of the propose…
Convolutional neural networks improve biopsy image classification for diagnosing Celiac Disease and Environmental Enteropathy.
problem Diagnosing Celiac Disease and Environmental Enteropathy from biopsy images due to histopathologic overlap.
method Proposed a convolutional neural network (CNN) to classify duodenal biopsy images.
result The proposed model achieves high accuracy in classifying biopsy images for CD, EE, and healthy controls.
New conjugate priors improve Bayesian inference for multinomial probit models.
problem Lack of tractable conjugate priors for efficient Bayesian inference in multinomial probit models.
method Unified skew-normal (SUN) distributions as conjugate priors, leading to improved posterior inference and classification.
result Improved computational methods for posterior inference and classification, especially in high dimensions.
Hierarchical CNNs improve diagnosis of GI diseases from histopathological images.
problem Diagnosing GI diseases from histopathological images is challenging due to heterogeneity and shared features.
method Embedded a class hierarchy into a VGGNet to address the hierarchical structure of GI diseases.
result The hierarchical model achieved better results than a flat model for multi-category diagnosis of GI disorders.
Study improves machine learning models for GI tract disease detection using comprehensive evaluations and cross-dataset testing.
problem Incomplete or incorrect evaluation of machine learning models for GI tract diseases.
method Comprehensive evaluations of five machine learning models using Global Features and Deep Neural Networks, introducing performance hexagons and cross-dataset testing.
result Demonstrates the need for more sophisticated performance metrics and evaluation methods to build generalizable models.
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.
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 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.
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.
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.
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.
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision he…
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.
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.
Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…
Deep neural network predicts health costs better than traditional models.
problem Accurate prediction of healthcare costs for optimal cost management.
method Developed a deep neural network to predict future health care costs from health insurance claims records.
result Deep neural network outperformed ridge regression and Morbi-RSA models in cost prediction.
GP-HD uses genetic programming to generate personalized health models.
problem Creating accurate, personalized health models from large health data.
method Genetic Programming framework to generate parameterized dynamical systems models.
result GP-HD models perform similarly to models based on domain knowledge and outperform LSTM models.
Examines fairness in ML for health, highlighting its importance and challenges.
problem Ensuring fairness in ML models for health to prevent health disparities.
method Reviews fairness notions in ML for health, including group, individual, and causal-based approaches.
result Discusses the importance and challenges of fairness in health-focused ML applications.
Generative adversarial networks transform streetscape images to improve health and wellbeing.
problem Improving health and wellbeing outcomes through better streetscape design.
method Generative adversarial networks were used to translate Google Street View images, preserving structure while changing the style from bad health to good health areas.
result Translated images show that good health areas have more green space and compact urban design, while good social capital areas have more footpaths and less fencing.
HealthSyn generates synthetic user behavior data for health interventions.
problem Lack of representative data for testing AI health interventions.
method Uses Markov processes to simulate diverse user actions, generating logs for ML algorithms.
result Synthetic data can be used to develop, test, and evaluate ML algorithms and RL-based interventions.
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…
Study shows racial bias in health data, which can be reduced with simple techniques.
problem Racial bias in health indicators measured by the Medical Expenditure Panel Survey (MEPS).
method Used publicly available and nationally representative MEPS data to show bias in predictive models for care management.
result Racial bias can be significantly reduced using simple mitigation techniques.
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.
This study evaluates a health knowledge graph for robustness in EHRs.
problem Evaluate robustness of a health knowledge graph in EHRs.
method Analyze sample size, unmeasured confounders, and non-linear functions.
result Identify sample size and unmeasured confounders as major sources of error.
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.
The paper introduces a health-informed policy gradient method for multi-agent reinforcement learning.
problem Optimizing joint reward functions in multi-agent systems with varying agent health.
method Health-informed credit assignment in a multi-agent proximal policy optimization algorithm.
result Significant improvement in learning performance compared to traditional methods.
DeepCoDA provides personalized interpretability for complex health data.
problem Interpreting complex health data, especially compositional data, is challenging.
method DeepCoDA framework for high-dimensional compositional data, personalized interpretability through patient-specific weights.
result DeepCoDA maintains state-of-the-art performance and provides coherent, personalized interpretations.
This study seeks to validate a search protocol of ill health-related terms using Twitter data which can later be used to understand if, and how, Twitter can reveal information on the current health situation. We extracted conversations related to health and disease postings on Twitter using a set of pre-defined keyword…
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.
Digital tools may hinder or facilitate multidisciplinary collaboration in occupational health.
problem Challenges in multidisciplinary collaboration in occupational health.
method Study of a precursor SPSTI's digital tools and their impacts.
result Digital tools can act as both brakes and levers for multidisciplinary dynamics.
Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tw…
New method for efficient personalized learning in mobile health.
problem Efficient and personalized learning in mobile health.
method Proposes a novel generative process on kernel composition for online Gaussian Process regression.
result Trajectories of kernel evolutions can be transferred between users to improve learning and kernels are meaningful for mHealth prediction.
Bayesian model predicts patient survival from sparse EHR data.
problem Analyzing EHR data with few samples and diverse information.
method Nonparametric probabilistic model using Bayesian trees.
result Improved survival trajectory predictions on patient 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.
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.
Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
A lack of information exists about the health issues of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people who are often excluded from national demographic assessments, health studies, and clinical trials. As a result, medical experts and researchers lack a holistic understanding of the health disparities fa…
Automated model assesses online health info quality using machine learning.
problem Low quality health information on the internet poses risks to patients.
method Used machine learning models, specifically hierarchical encoder attention-based neural networks (HEA) with BERT and BioBERT embeddings.
result HEA models outperform traditional models in evaluating health info quality.
Improved disability insurance model with collective health claims.
problem Enhance disability insurance model with collective health claims.
method Expand classic semi-Markov model with collective health claims, solve many-body problem using mean-field approach.
result Mean-field approach simplifies complex model into a transparent pricing method.
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.
Semiparametric STAR model improves mental health data analysis.
problem Overdispersed, zero-inflated, bounded count data in self-reported mental health surveys.
method STAR transformation and rounding of latent Gaussian model, nonparametric transformation estimation, EM algorithm for maximum likelihood.
result Substantial improvements in goodness-of-fit compared to existing models.
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.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
New models automate support group formation in online health communities.
problem Challenges in traditional support group formation methods for scalability, static categorization, and insufficient personalization.
method Two novel machine learning models: gDMR and gSTM, integrating user content, demographics, and network data.
result Models outperform baselines in predictive accuracy, semantic coherence, and internal group consistency.
Study on privacy-preserving health care models that sacrifice accuracy for data protection.
problem Privacy-preserving models in health care neglect data from the tails, reducing accuracy for small groups.
method Used state-of-the-art differentially private learning methods for clinical prediction tasks.
result Privacy-preserving models in health care exhibit steep tradeoffs between privacy and utility, and disproportionately influence large demographic groups.