Paper presents a new method for clustering patient records using tensor decomposition.
problem Clustering high-dimensional binary data, especially in healthcare records.
method Tensor decomposition for an efficient and robust heuristic.
result Clinically meaningful results obtained on two healthcare datasets.
This paper surveys deep learning applications in EHR analysis.
problem Leveraging EHR data for clinical informatics tasks.
method Reviews deep learning architectures and techniques applied to EHR data.
result Identifies current limitations and future research directions.
CorGAN generates synthetic healthcare records while preserving privacy.
problem Generating realistic synthetic healthcare records while maintaining privacy.
method Combining Convolutional Generative Adversarial Networks and Convolutional Autoencoders to capture correlations between medical features.
result CorGAN generates synthetic data with performance similar to real data in various ML settings.
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.
Enhances detection of adverse drug events using diverse healthcare record data.
problem Detecting adverse drug events from mixed data types in electronic health records.
method Aggregate diagnosis codes, drug codes, and lab measurements; use recursive feature selection.
result Significant improvement in AUC using additional features, statistically significant.
Neural TPPs improve EHR modelling efficiency.
problem Challenges in modelling irregularly timed, noisy EHRs.
method Proposed neural network parameterizations of Temporal Point Processes (TPPs) for EHRs.
result Neural TPPs outperform non-TPP models on EHRs.
MiME learns EHR data structure for predictive healthcare tasks.
problem Data insufficiency in EHR for predictive healthcare tasks.
method Leverages multilevel structure of EHR data and learns multilevel embedding.
result MiME outperforms baseline methods in diverse evaluation settings.
Generative adversarial networks enhance risk prediction in EHRs.
problem Limited labeled data in EHRs for deep learning risk prediction.
method ehrGAN, a modified GAN, generates realistic EHR data for semi-supervised learning.
result Significant improvements in classification tasks with generated data over baselines.
TAPER learns unified patient EHR representations for healthcare tasks.
problem Irregular and multimodal data in electronic health records.
method Transformer networks and BERT for embedding structured and unstructured data.
result TAPER model outperforms on mortality, readmission, and length of stay tasks.
Proposes a causal regularizer for interpretable predictive models.
problem Accurate predictive models that are also causally interpretable in healthcare.
method Causal regularizer applied to neural network architecture for non-linear causality analysis.
result Causally-regularized model outperforms L1-regularized counterpart in causal accuracy and predictive performance.
Machine learning faces challenges in healthcare data, but offers opportunities.
problem Poorly labeled data, multiple endotypes, and underrepresented healthy individuals.
method Review of existing machine learning methods and challenges in healthcare.
result Opportunities for machine learning in healthcare identified.
Personalized healthcare predictions using deep mixed effect model with Gaussian Processes.
problem Making personalized and reliable predictions from time-series data in healthcare.
method A composite model combining a deep neural network for global trends and Gaussian Processes for individual variability.
result Practical advantages over standard time-series deep models, demonstrated on diverse EHR datasets.
Generates synthetic EHR chief complaints from structured data.
problem Generating unstructured text in EHRs.
method Encoder-decoder deep learning model trained on EHR data.
result Synthetic chief complaints preserve epidemiological information.
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.
ME2Vec learns medical entity vectors from EHR data.
problem Learning structured information from EHR data.
method Graph-based medical entity embedding using diverse graph embedding techniques.
result ME2Vec outperforms baselines in disease diagnosis prediction.
Study finds multi-task learning and pre-training can improve healthcare models on EHR data.
problem Improving model performance on diverse EHR tasks using multi-task learning.
method Examined multi-task learning across various EHR tasks and training schemes, using pre-training and fine-tuning.
result Significant gains in model performance achieved via multi-task pre-training and single-task fine-tuning.
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.
MIMIC-Extract transforms EHR data for reproducible healthcare machine learning.
problem Lack of accessible, standardized healthcare data for machine learning.
method Open-source pipeline for converting raw EHR data into usable dataframes.
result Demonstrates utility through benchmark tasks and baseline results.
Representation learning improves EHR data for healthcare tasks.
problem Transforming EHR data into useful representations for machine learning.
method Deep learning and disentangling underlying factors from EHR data.
result Better representations improve machine learning performance in healthcare.
Paper proposes GANs for predicting drug-induced lab test trajectories in EHRs.
problem Predicting drug-induced lab test trajectories in EHRs.
method Proposes a framework for GANs in healthcare, measures predictive power of synthetic data, and incorporates representation learning.
result Incorporating representation learning improves GANs' predictive power for drug-induced lab test trajectories.
BEHRT improves disease prediction in EHRs by 8-10%.
problem Early disease detection in EHRs for better patient outcomes.
method Deep neural sequence transduction model for EHRs.
result BEHRT improves average precision by 8.0-10.8% compared to state-of-the-art models.
Enhances understanding of patient healthcare journeys using self-attention.
problem Capturing hidden dependencies in multi-level patient journey data.
method Proposes a multi-level self-attention network (MusaNet) for encoding patient journeys.
result MusaNet produces higher-quality representations than state-of-the-art methods.
PHI Scrubber removes sensitive patient info from medical notes.
problem Protecting patient privacy in electronic health records.
method Deep learning model with de-convolutional neural network and bi-directional LSTM-CNN.
result Eliminates individually identifiable information from physician notes.
Model learns hierarchical EHR representation for clinical outcome prediction.
problem Capturing temporal patterns in irregular clinical event sequences.
method Proposes differentiated mechanisms to model events at different time scales, learning hierarchical representations.
result Significantly improves clinical outcome prediction, achieving AUC scores of 0.94 and 0.90 for death and ICU admission respectively.
Causal thinking improves healthcare decisions from EHRs.
problem Shortcuts in data lead to biased healthcare decisions.
method Step-by-step framework for valid decision making from EHRs.
result Valid decision making requires careful analysis of EHR data.
An unsupervised method clusters patient incident reports for content analysis.
problem Lack of methods to extract interpretable content from electronic healthcare records.
method Combines text-embedding with paragraph vectors and graph-theoretical multiscale community detection.
result Extracts high-intrinsic-consistency groups of patient incident reports.
This study predicts diabetes complications using financial records and neural networks.
problem Managing chronic diseases like diabetes in patients.
method Used financial records from health plans, applied self-attentive recurrent neural networks.
result Successfully predicted diabetes complications with an AUC of 0.81-0.94, 60-240 days ahead.
Deep learning and active learning improve survival analysis for prostate cancer treatment recommendations.
problem Building accurate survival models from high-dimensional, sparse, and complex clinical data.
method Deep active survival analysis framework using labeled and unlabeled instances, active learning with oracle.
result Our approach outperforms baseline models in prostate cancer treatment recommendation.
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.
MPVAA learns holistic patient representations from mixed healthcare data.
problem Learning personalized patient representations from heterogeneous healthcare data.
method Mixed Pooling Multi-View Attention Autoencoder (MPVAA) that integrates non-linear relationships among multiple data modalities.
result MPVAA generates more effective patient representations than state-of-the-art methods.
ConCare personalizes healthcare predictions by capturing EMR features.
problem Predicting patient outcomes from EMR data with personalization.
method Captures personal characteristics and time-aware distribution in EMR data.
result Improves healthcare prediction accuracy through personalized health context.
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 fair clinical risk prediction models using counterfactual reasoning.
problem Addressing biases in clinical risk prediction models for underrepresented groups.
method Augmented counterfactual fairness criteria applied to electronic health records data.
result Demonstrates the feasibility of fair clinical risk prediction models using counterfactual inference.
PEHRT harmonizes EHR data for translational research.
problem Barriers in using EHR data for translational research.
method Common pipeline including open-source code, visualization tools, and detailed documentation.
result PEHRT harmonizes EHR data to standardized ontologies and generates robust embeddings.
AACE learns treatment policies from EHRs using annotations to improve accuracy.
problem Learning treatment policies from multimodal EHRs with bias and inefficiency.
method Annotation-assisted coarsened effects (AACE) method.
result AACE outperforms existing methods in predicting treatment benefit from multimodal EHRs.
Proposes statistical inference for dependency knowledge graphs from EHR data.
problem Statistical uncertainty in linking entities in EHR data.
method Dynamic log-linear topic model with singular value decomposition.
result Established asymptotic normality for sparse graph edge recovery.
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.
Machine learning in healthcare faces challenges due to complex data attributes.
problem Complex data attributes hinder accurate insights from machine learning models.
method Discusses preprocessing, model building, and interpretation challenges.
result Understanding data attributes is crucial for successful machine learning in healthcare.
Somed2Vec learns medical concept embeddings from SNOMED-CT, improving healthcare analytics.
problem Lack of effective vector representations for medical concepts in healthcare analytics.
method Graph-based representation learning using random walks and Poincaré embeddings on SNOMED-CT.
result Concept embeddings from SNOMED-CT significantly outperform state-of-the-art embeddings.
Super Learner improves prediction in electronic healthcare databases.
problem Improving prediction models in large electronic healthcare databases.
method Super Learner ensemble learning algorithm combined with high-dimensional propensity score variable selection.
result Super Learner adapts to different datasets and optimizes predictive performance.
Method explains machine learning diabetes risk predictions.
problem Lack of interpretability in machine learning models.
method Automatic explanation method for machine learning models.
result 87.4% of patients' predictions explained without accuracy loss.
MCRAGE generates synthetic data to balance healthcare datasets.
problem Imbalanced datasets in healthcare lead to biased model performance for minority groups.
method Generative modeling to create synthetic data for underrepresented classes.
result MCRAGE improves model performance on minority groups.
The paper compares different deep architectures for feature learning from EHRs.
problem Extracting meaningful insights from high-dimensional, sparse clinical data.
method Uses stacked sparse autoencoders, deep belief networks, adversarial autoencoders, and variational autoencoders for feature representation.
result Stacked sparse autoencoders perform better for small data sets, while variational autoencoders outperform for large data sets.
This paper compares different deep learning techniques for feature learning from EHRs.
problem Extracting meaningful insights from high-dimensional, sparse clinical data.
method Uses stacked sparse autoencoders, deep belief networks, adversarial autoencoders, and variational autoencoders for feature representation.
result Variational autoencoders outperform other methods for large data sets, while stacked sparse autoencoders are superior for small data sets.
New algorithm improves imputation of missing clinical data.
problem Missing data in longitudinal healthcare studies.
method MedImpute algorithm for imputing continuous and categorical features in multivariate panel data.
result Significant improvements in imputation accuracy and model performance.
ELMV uses ensemble learning to handle missing values in EHR data.
problem Significant missing values in EHR data cause bias and unreliable conclusions.
method ELMV constructs multiple subsets with lower missing rates and uses a support set for ensemble learning.
result ELMV outperforms conventional methods in critical feature identification and outcome prediction.
New method improves prediction accuracy for low-risk patients in healthcare.
problem Machine learning models often focus on high-risk patients, ignoring low-risk ones.
method Proposed a new log-likelihood formulation to minimize proportional rate error.
result Improved prediction accuracy for low-risk patients in EHR data.
Systematic review of electronic health record phenotyping approaches.
problem Detecting patient cohorts using electronic health records.
method Comprehensive literature review of preprocessing and modeling approaches.
result Natural language processing shows promise for electronic phenotyping.