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…
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48 results for “health information”
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.
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…
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.
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.
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.
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.
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.
Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we deve…
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is t…
Feature selection, which searches for the most representative features in observed data, is critical for health data analysis. Unlike feature extraction, such as PCA and autoencoder based methods, feature selection preserves interpretability, meaning that the selected features provide direct information about certain h…
Risk-based active learning improves SHM decision-making.
problem Lack of prior labels for structural health monitoring.
method Risk-based active learning approach to guide data labeling.
result Improves decision-maker's performance in SHM.
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…
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.
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
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.
Proposes a model to handle mobile health data with irregular measurements.
problem Handling heterogeneous, multi-resolution data in mobile health.
method Individualized dynamic latent factor model for irregular multi-resolution time series data.
result Superior performance compared to existing methods in simulation and smartwatch data applications.
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.
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.
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…
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…
Deep learning system improves accuracy of food packaging date verification.
problem Improper labeling of food packaging poses health risks.
method Multi-source deep domain adaptation for domain-invariant representations and class boundary alignment.
result Significant improvement in classification accuracy of use-by date verification.
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators…
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.
Physics-informed model predicts beam stiffness and monitors structural health.
problem Predicting and monitoring the stiffness of Euler-Bernoulli beams.
method Physics-informed Gaussian process model using the Euler-Bernoulli beam equation.
result Model accurately predicts bending stiffness and detects structural damage.
The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have b…
Machine learning constructs problem-based medical records from electronic health records.
problem Difficulty in finding relevant medical information for clinical questions.
method Knowledge base completion using machine learning on electronic health records.
result Automatic construction of problem-based groupings of medications, procedures, and lab tests.
A latent topic model for mining heterogenous non-randomly missing electronic health records datacs.LG
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised g…
Kernel TCK_IM tackles missing data in EHR time series, improving analysis.
problem Missing data complicates analysis of EHR time series.
method TCK_IM kernel using ensemble learning of mixed mode Bayesian mixture models.
result TCK_IM kernel effectively exploits missing data without imputation.
Simulation-based inference aids in predicting disease dynamics for health policy.
problem Predicting disease dynamics to inform public health interventions.
method Simulation-based inference using machine learning.
result Potential for efficient interpretable Bayesian inference in epidemiological 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.
Proposes a deep spectral Q-learning for mobile health data.
problem Personalized treatment assignment for patients with time-varying covariates.
method Integrates PCA with deep Q-learning for mixed frequency data.
result Mean return converges to optimal under estimated optimal policy.
Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich …
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.
Framework improves health by planning actionable treatment processes.
problem Developing objective treatment processes in clinical settings.
method Surrogate Bayesian model combined with ML for personalized health improvement.
result Computed treatment processes are actionable and consistent with clinical knowledge.
The increased adoption of Electronic Health Records(EHRs) has brought changes to the way the patient care is carried out. The rich heterogeneous and temporal data space stored in EHRs can be leveraged by machine learning models to capture the underlying information and make clinically relevant predictions. This can be …
Improved TF-IDF for word relevance in health-care social media documents.
problem Determining word relevance in informal documents.
method Semantic Sensitive TF-IDF (STF-IDF) method.
result Decreased TF-IDF mean error rate by 50% to 13.7%.
Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobi…
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the a…
The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient hea…
Bayesian model enhances phenotype discovery in asthma EHRs.
problem Lack of interpretability in unsupervised learning phenotyping of EHR data.
method Operationalized a Bayesian latent class framework with clinical knowledge priors.
result Identified an asthma sub-phenotype with elevated eosinophil levels and allergy markers.
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models…
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.
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…
This paper studies an intelligent ultimate technique for health-monitoring and prognostic of common rotary machine components, particularly bearings. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse auto-encoder. Then, the correlation of the ex…
Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learningstat.ML
While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in another due to variability in features; even within disease labels there can be differences (e.g. "fever" may mean something different repor…
The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR). While primarily designed for archiving patient clinical information and administrative healthcare tasks, many researchers have found secondary use of these records for various clinical informatics tasks…