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.
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.
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.
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.
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.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.
ML4H workshop at NeurIPS 2018 focuses on health applications of machine learning.
problem Improving health outcomes through machine learning.
method Presented health applications and machine learning techniques.
result Demonstrated the potential of machine learning in healthcare.
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.
Supervised learning improves disease outbreak detection accuracy.
problem Early detection of infectious disease outbreaks to protect public health.
method Developed a supervised learning approach based on hidden Markov models for disease outbreak detection.
result Reduces false positive rate by up to 50% while maintaining sensitivity.
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.
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.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…
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,…
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.
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 …
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.
New method clusters health codes for better risk adjustment.
problem Improving insurer incentives to attract lower-cost enrollees.
method Markov Chain Monte Carlo methods for clustering diagnostic codes.
result Methodology outperforms common alternatives in health expenditure prediction.
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-l…
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…
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
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.
App enhances midwives' skills in low-income countries.
problem Low-income countries have high maternal and child mortality rates.
method Deep learning models predict midwives' interest in learning materials.
result Deep learning models accurately predict midwives' interest in learning materials.
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 system predicts acute critical illness from EHRs with explainability.
problem Lack of clinical interpretability in AI predictions for acute critical illness.
method Developed an explainable AI early warning score (xAI-EWS) system.
result System provides clinicians with insights into EHR data explaining predictions.
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.
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.
In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging proble…
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.
Cardea automates machine learning for EHRs, improving model building efficiency.
problem Lack of a trusted, open-source framework for automated machine learning in EHRs.
method Uses FHIR for data structure, AUTOML frameworks for feature engineering, model selection, and tuning, and an adaptive data assembler.
result Demonstrates framework's effectiveness on 5 prediction tasks, highlighting its flexibility and human competitiveness.
Novel imputation method for EHRs with structured and sporadic missingness.
problem Missing data in integrated EHR datasets for clinical applications.
method Macomss, a novel imputation framework for structurally and heterogeneously missing data.
result Macomss outperforms existing methods in imputation and downstream prediction accuracy.
Deep learning predicts employment changes and industry health.
problem Forecasting short-term employment changes and assessing long-term industry health.
method LSTNet, a multi-scale deep learning model, processes multivariate time series data.
result LSTNet outperforms baseline models in most sectors, especially stable ones.
The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier level. We present a large-scale longitudinal study (18 months) of the DDI phenom…
Paper presents a data preprocessing method for PHM models.
problem Lack of consistent data preprocessing for PHM applications.
method Comprehensive pipeline for sensor data preprocessing.
result Creation of clean data sets for training machinery health state classifiers.
System predicts HIV patients at risk of dropping out of care.
problem High non-adherence and dropout rates among HIV patients.
method Predictive machine learning model based on resource constraints, stability, and fairness.
result Model performs 3x better than baseline for clinical use and 2.3x better for city-wide use.
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.
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.
System recommends workouts and predicts success rates using RNNs.
problem Promoting healthy lifestyles through personalized exercise recommendations.
method Two interconnected recurrent neural networks (RNNs) using historical workout data.
result Interconnected-RNN model predicts exercise success rates with improved accuracy.
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…
Enhanced word embedding creates new consumer-friendly health terms.
problem Laymen's health terms are often jargon and hard to understand.
method Developed an enhanced GloVe word embedding technique to generate new consumer-friendly terms.
result New CHV terms generated from consumer-generated text.
Discriminative classifiers improve decision-making in SHM systems.
problem Lack of descriptive labels for SHM data.
method Risk-based active learning with discriminative classifiers.
result Discriminative classifiers offer improved robustness and reduced inspection costs.
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…
Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability a…
Machine learning models outperform traditional actuarial methods in predicting health insurance costs.
problem Improving accuracy in health insurance pricing to identify concession opportunities.
method Developed and evaluated two machine learning models at the patient and employer-group levels.
result Machine learning models outperformed traditional actuarial models by 20% in predicting costs.
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.
Text2Node maps medical phrases to a taxonomy, overcoming coding standard limitations.
problem Limited data interchangeability between EHR systems due to different coding standards.
method Text2Node uses word and node embeddings, along with mapping functions, to generalize from limited training data.
result Text2Node achieves high accuracy in mapping phrases to a taxonomy, even for unseen concepts.
Develops algorithms to balance personalization and statistical power in mobile health studies.
problem Balancing personalization and statistical power in mobile health studies.
method Develops general meta-algorithms to modify existing bandit algorithms.
result Guarantees sufficient power while improving user well-being.
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…
Deep Claim predicts payer responses from claims data using deep learning.
problem Predicting payer responses from claims data to improve healthcare performance.
method Learning complex dependencies in claim inputs to create a compact representation, then using deep learning to predict responses.
result Deep Claim improves claim denial prediction by 22.21%.