Machine Learning in Healthcare

The paper introduces a graph-embedded topic model to analyze UK Biobank data, identifying pain phenotypes and revealing cardiovascular-related medication and condition patterns.

The paper presents a modular pipeline for federated health data analysis using deep learning to predict health outcomes across diseases.

SCORNET estimates survival curves using EHR data with noisy event times and limited current status labels, achieving efficiency similar to Weibull regression with non-parametric flexibility.

This paper uses deep learning to predict pancreatic cancer risk from clinical disease trajectories in two large datasets, achieving higher accuracy than models without temporal information.

The paper develops a model to identify patients likely to have a genetic disease using EHR data, achieving high accuracy in predicting genetic testing needs.

The paper enhances a COVID-19 dataset by adding weather and research sentiment features, achieving 95% accuracy in survival prediction using XGBoost.

This paper develops and compares machine learning models to predict 30-day unplanned readmission in frail elderly patients using electronic health records.

The paper compares machine learning algorithms for identifying acute ischemic stroke patients using EHR data, finding high accuracy with logistic regression and demonstrating generalizability.

This study develops a high-throughput phenotyping method using temporal sequences from EHR data, achieving superior classification performance compared to conventional methods.

This paper develops indication embeddings to reduce health data dimensionality, improving cohort matching for drug safety studies by capturing relevant treatment decision information.

sureLDA automates phenotype labeling in EHR data using topic modeling, achieving high accuracy and robustness across various phenotypes.

This study uses UMLS-driven NLP on EHR data to identify influenza predictors, finding that complex models with NLP-enhanced data outperform simpler models.

This study compares prediction models for 30-day hospital readmissions, finding that artificial neural networks with medical code embeddings improve accuracy compared to logistic regression.

The paper develops and validates phenotype classifiers across multiple sites using machine learning, showing good portability within the US but limited international generalizability.

This paper validates a machine learning algorithm for predicting 48-hour all-cause mortality across multiple centers, showing high AUROC values compared to qSOFA and MEWS.

This paper develops tensor factorization-based models to predict individual chronic disease risk using EHR data, achieving 60% recall in top 10 predictions.

SALTClass improves classification of sparse clinical text by using clustering and smoothing on unlabeled data to enhance representation for supervised classification tasks.

The paper integrates biomedical research with electronic health records to create biologically meaningful machine-readable embeddings for precision medicine.

This paper uses LSTM-based RNNs to predict ICU readmissions from EHR data, achieving higher sensitivity and AUC than traditional methods.

This study shows that natural language processing applied to electronic health records can accurately identify patients with non-alcoholic fatty liver disease and detect those at risk for disease progression.

This study analyzes 70 EHR phenotyping algorithms from CALIBER and UK Biobank to understand their heterogeneity and identify common implementation patterns for developing a minimum information standard.

The paper develops and evaluates prediction models for cardiovascular diseases and type 2 diabetes using EHR data, showing high discrimination performance for most conditions.

This paper develops an automated high-throughput phenotyping method (MAP) that improves phenotype accuracy and scalability for PheWAS studies.

The paper models caregiver networks around individual patients to predict clinical outcomes like hospital stay and mortality.

This paper introduces a method to remove confounding factors in deep neural networks to improve prediction accuracy in healthcare applications.

This paper compares a deep bidirectional RNN end-to-end model with traditional classifiers for smoking status extraction from Spanish clinical notes, finding the RNN achieves high accuracy with significantly less development time.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

The paper improves imputation of missing lab measures in IBD EHR data using latent-based analysis and clustering of comorbidities.

The paper develops a topic extraction pipeline from psychiatric EHRs to support readmission risk prediction, showing initial results and future feature plans.

This paper develops a composite mixture model to analyze sepsis EMR data, identifying mortality risk trends and subgroups.