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.
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.
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.
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.
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.
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.
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.
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.
Stable health predictions need deconfounding test set features.
problem Stability of predictions in health machine learning is compromised by selection biases.
method Deconfounding the test set features improves prediction stability across different environments.
result Improved stability achieved by deconfounding test set features.
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.
Model predicts depression and anxiety from social network dynamics.
problem Predicting mental health from social interactions.
method Developed a predictive model using dynamic social network data.
result Dynamic social network model outperforms static models.
Patient2Vec learns personalized deep EHR representations for better health outcomes prediction.
problem Complexities in EHR data hinder machine learning applications.
method Patient2Vec: A personalized deep learning framework for longitudinal EHR data.
result Patient2Vec achieves AUC of 0.799 in predicting future hospitalizations, outperforming baseline methods.
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 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.
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.
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%).
Deep learning predicts drug prescriptions across global health records.
problem Predicting drug prescriptions in chronic disease patients.
method Adaptive cross-global attention graph kernel network with support vector machine.
result Model outperforms current methods in accuracy and interpretability.
Refines diagnostic prediction using causal relationships.
problem Improving accuracy of pain diagnostics prediction.
method Two approaches: 1) Inference of causal relationships, 2) Post-processing refinement.
result Potential for improving pain diagnostics prediction accuracy.
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.
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.
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.
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.
Paper proposes methods to predict hard drive health using machine learning, improving accuracy and predictive time.
problem Predicting hard drive health with high accuracy from imbalanced SMART datasets.
method Layerwise perturbation-based adversarial training and semi-supervised learning.
result The model can predict hard drive health status 5-15 days in advance.
EKG-based models show better stability across patient populations than EHR-based models.
problem Model generalization issues in EHR and EKG-based predictive models.
method Two tests to measure model generalization, comparing EHR and EKG data.
result EKG-based models are more stable across different patient populations.
Learn embeddings from EHRs to predict ICD codes.
problem Predicting ICD codes from patient visits in EHRs.
method Deep neural network trained to predict ICD codes, capturing clinical information.
result Embeddings capture relevant clinical information and can be used in machine learning models.
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
mixEHR model reveals latent disease topics from EHR data.
problem Mining heterogeneous, non-randomly missing EHR data.
method Generative model integrating collaborative filtering and latent topic models.
result mixEHR reveals meaningful multi-disease insights and outperforms previous methods.
Paper predicts medication non-adherence in cancer patients using ML.
problem Predicting and understanding medication non-adherence in cancer patients.
method Developed ML models to predict non-adherence, fine-tuned by oncologists.
result Improved support for cancer patients through ML risk scores.
The study predicts health risks of young migrants using machine learning.
problem Predicting health risks of young migrants in data-constrained environments.
method Designing a webapp for stakeholders, curating an artificially curated dataset, and experimenting with machine learning models.
result Machine learning can assist in identifying vulnerable migrants and critical factors of migration.
Paper proposes sharing models instead of data for smart health predictions.
problem Sharing sensitive medical data is legally restricted and challenging.
method Train a teacher model on sensitive data, then transfer its knowledge to a student model without accessing original data.
result Student model mimics teacher model performance in making accurate predictions.
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.
Develops machine learning models for clinical predictions from complex health data.
problem Complexity and uncertainty in medical data.
method Statistical Relational Learning approaches.
result Outstanding performance in medical research and real-world applications.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
AI improves healthcare diagnostics and predictions.
problem Data heterogeneity and model limitations in AI for health.
method Review of AI applications in health informatics.
result AI enhances disease diagnosis and prediction.
Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.
problem Identifying meaningful relationships between environmental features and health outcomes on an individual level.
method Contrastive clustering framework with stick-breaking prior and participant-specific prediction loss.
result Model effectively identifies distinct personal environments and groups them into meaningful types linked to health outcomes.
Generative model predicts menstrual cycle lengths accounting for self-tracking artifacts.
problem Uncertainty in self-tracked health data due to user adherence.
method Hierarchical, generative model using machine learning.
result Model yields state-of-the-art performance in predicting menstrual cycle lengths.
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.
Deep learning predicts opioid use disorder risk in patients.
problem Identifying patients at high risk of opioid use disorder.
method Applied LSTM models to analyze electronic health records of opioid users.
result LSTM model outperformed other methods with F1 score of 0.8023 and AUCROC of 0.9369.
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,…
Language models improve clinical prediction models using EHR data.
problem Limited patient data for training clinical prediction models.
method Using patient representation schemes from natural language processing.
result 3.5% mean improvement in AUROC on five prediction tasks.
Deep CNN model predicts neuronal cell health from images.
problem Predicting the biological activity of chemical compounds on neuronal cells.
method Deep convolutional neural network (CNN) with residual connections.
result Achieved 99.6% accuracy in distinguishing treated from untreated cells.
MetaPred uses meta-learning to improve clinical risk prediction from limited EHR data.
problem Clinical risk prediction from sparse patient EHR data.
method Meta-learning approach to train a meta-learner from related tasks, then fine-tune for target risk prediction.
result MetaPred achieves better performance for target risk prediction with limited data.
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.
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.
Satellite images predict U.S. county mortality rates.
problem Predicting mortality rates in U.S. counties using satellite imagery.
method Convolutional neural network trained on crude mortality rates, learned features interpreted using Shapley Additive Feature Explanations.
result Predicted mortality from satellite images correlated strongly with true mortality rates (Pearson r=0.72).
Proposes a new model for EHR data using time-dependent Gaussian processes.
problem Joint modeling of multiple clinical variables over time.
method Multivariate nonstationary Gaussian processes with time-varying parameters and posterior inference via HMC.
result The proposed model outperforms stationary models and reveals latent correlations predictive of patient risk.
Bayesian deep learning predicts uncertainty in EHRs for better healthcare decisions.
problem Lack of interpretability and trustworthiness in deep learning models for EHRs.
method Proposes a Bayesian Neural Network (BNN) model to predict uncertainty in EHRs.
result High uncertainty instances harm model performance; distributions reveal patients for timely intervention.
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.