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.
Deepr learns features from medical records to predict patient risk.
problem Feature engineering bottleneck in creating predictive systems from medical records.
method Transforms medical records into sequences, uses convolutional neural nets to detect and combine clinical motifs.
result Deepr achieves superior accuracy in predicting patient risk compared to traditional techniques.
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.
Method learns clinical conditions from noisy labels without gold-standard data.
problem Learning clinical conditions from noisy labels without perfect data.
method Joint probabilistic graphical models with moments-based initialization.
result Model outperforms baselines in assigning heldout clinical conditions.
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.
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.
Paper develops deep learning for predicting clinical endpoints from diverse medical records.
problem Predicting clinical endpoints from heterogeneous, irregularly visiting medical records.
method Proposes a novel model with a new gate to control visiting rates of different events.
result Model effectively predicts death and abnormal lab tests with real-world clinical data.
New method uses probabilistic independence to discover disease signatures from medical records.
problem Insufficiently precise diagnosis of clinical disease leading to treatment failures.
method Unsupervised machine learning using probabilistic independence to disentangle disease patterns.
result Inferred 2000 clinical disease signatures from medical records, improving cancer prediction.
Clinical models trained on EHRs degrade in performance over time due to data drift.
problem Model performance degradation over time in clinical settings.
method Accessed year of care for each record in MIMIC, aggregated features into clinical concepts, and tested mitigation strategies.
result State-of-the-art models show significant performance drops when tested on future data compared to historical data.
Model predicts patient trajectories and interventions from EMR data.
problem Forecasting patient outcomes from EMR data.
method Deep state space generative model capturing latent state dynamics.
result Model outperforms state-of-the-art methods on real EMR data.
Enhances generative model for clinical data privacy and accuracy.
problem Data privacy in electronic patient records.
method Improves a time-series generative model with privacy safeguards.
result DP-TimeGAN achieves a mean authenticity of 0.778 on the CKD dataset.
Paper proposes a streamlined approach to clinical concept extraction using LSTM-CRF.
problem Automated extraction of concepts from clinical records for clinical research.
method Bidirectional LSTM with CRF decoding initialized with general-purpose word embeddings.
result Experimental results outperform all recent methods and rank closely to the best submission from the original i2b2/VA challenge.
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.
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.
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.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
Predict sepsis early from EHR data with aggregated clinical events.
problem Predict sepsis from clinical data in EHR with temporal interactions.
method Aggregates heterogeneous clinical events, captures temporal interactions with LSTM.
result Achieved high utility score (0.321) in PhysioNet/Computing in Cardiology Challenge 2019.
New algorithm identifies multiple medical conditions from EHRs.
problem Automatically phenotyping multiple medical conditions from clinical notes.
method Constrained Non-Negative Matrix Factorization (NMF) with domain-specific constraints.
result Learned phenotypes are clinically interpretable and predictive of mortality.
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.
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.
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.
Model identifies key problems in HIV patients' records.
problem Complex and time-consuming task of identifying patient problems from electronic health records.
method Unsupervised phenotyping approach that jointly learns phenotypes from structured and unstructured data.
result Learned phenotypes and their relatedness are clinically valid and surpass existing methods.
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.
Framework improves clinical timeline reconstruction from text and tables.
problem Temporal precision and event timing in clinical narratives and EHRs.
method Retrieval-augmented multimodal alignment framework.
result Consistently improves absolute timestamp accuracy and temporal concordance.
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.
Paper proposes multimodal contrastive learning for EHR data.
problem Separate treatment of structured and unstructured EHR data.
method Proposes a multimodal feature embedding generative model and a multimodal contrastive loss.
result Multimodal learning yields better feature representation than single-modality learning.
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.
New deep learning model tackles sparse clinical data.
problem Sparse and irregularly sampled multivariate time series in EHRs.
method Semi-parametric interpolation network followed by prediction network.
result Improved mortality and length of stay predictions.
HTAD predicts diagnoses from EHRs using target-aware attention.
problem Lack of specificity in aggregating clinical records for diagnosis prediction.
method Hierarchical target-attentive mechanism for EHRs.
result Target-aware attention significantly improves diagnosis prediction performance.
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.
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…
Proposes a deep learning framework for evaluating patient similarities from EHRs.
problem Evaluating clinical similarities between patients for various healthcare applications.
method A deep learning framework with medical concept embedding, preserving temporal information.
result Significant improvement in patient similarity evaluation over baselines.
Develops a new model for analyzing clinical data with irregular sampling.
problem Analyzing irregularly sampled, temporally correlated clinical data.
method Hidden Absorbing Semi-Markov Model (HASMM) with a novel EM algorithm and forward-filtering algorithm.
result Demonstrates improved diagnostic and prognostic utility in critical care settings.
AdaptHetero uses MLI to tailor EHR models for subgroup-specific predictions.
problem Lack of subgroup-specific, operationalizable modeling strategies in EHRs.
method Integrates MLI with unsupervised clustering to identify subgroup-specific characteristics.
result Improves predictive performance by up to 174.39 percent across many subpopulations.
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 dataset from clinicians improves sepsis prediction models.
problem Circularity in previous sepsis prediction models.
method Developed an independent dataset from clinical judgments, avoiding circularity.
result Achieved state-of-the-art AUROC scores.
Neural network predicts cardiovascular events from EHRs with high accuracy.
problem Predicting onset of cardiovascular diseases from electronic health records.
method Multi-task gated recurrent units with attention mechanism.
result Model outperforms clinical risk scores in predicting stroke and myocardial infarction.
Framework harmonizes EHR data across institutions for better analysis.
problem Heterogeneity of medical codes and terminologies hinder EHR data analysis.
method MASH (Multi-source Automated Structured Hierarchy) uses neural optimal transport and learned hyperbolic embeddings to align and structure EHR data.
result MASH generates interpretable hierarchical graphs for unstructured local laboratory codes.
Study compares federated and centralized learning for patient data privacy.
problem Ensuring privacy in machine learning models trained on electronic health records.
method Examined private and non-private federated learning for clinical prediction tasks.
result Differentially private stochastic gradient descent is effective in centralized learning but challenging in federated learning.
Paper introduces a method to distill interpretable phenotypes from deep learning models for healthcare.
problem Lack of interpretability in deep learning models for clinical decision-making.
method Interpretable Mimic Learning using Gradient Boosting Trees.
result Obtains similar or better performance than deep learning models while providing interpretable phenotypes.
Study validates machine learning models for patient outcomes using various methods.
problem Validating machine learning models for patient outcomes in electronic health records.
method Used three state-of-the-art machine learning methods (random forest, gradient boosting, logistic regression) to predict patient outcomes and assess feature importance.
result Permutation tests applied to random forest and gradient boosting models showed the most agreement with clinical interpretation of feature importance.
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.
Causal ML predicts treatment outcomes, aiding personalized medicine.
problem Predicting individualized treatment effects for personalized medicine.
method Flexible, data-driven methods using causal inference with clinical trial and real-world data.
result Causal ML allows for estimating individualized treatment effects.
Late fusion of clinical notes and physiological data improves ICU mortality prediction.
problem Improving ICU mortality prediction using multimodal data.
method Late fusion of clinical notes and physiological time series data with a deep learning architecture.
result Late fusion approach provides statistically significant improvement in mortality prediction performance.
The study creates benchmarks for clinical time series data to evaluate machine learning models.
problem Lack of publicly available benchmark data sets for healthcare research.
method Proposed four clinical prediction benchmarks using MIMIC-III data, evaluated various deep supervision and multitask training methods.
result Demonstrated the effectiveness of deep supervision, multitask training, and data-specific architectural modifications on neural models.
Scrubbing PHI data from medical records is now efficient and scalable with SpaCy.
problem Efficiency and scalability of de-identification techniques for PHI data.
method Evaluated numerous deep learning techniques including SpaCy for performance and efficiency.
result SpaCy model is both well performing and extremely efficient for PHI data scrubbing.
Predicting ICU patient mortality using heart rate signals.
problem Early prediction of ICU patient mortality.
method Features extracted from heart rate signals, fed into eight classifiers.
result Decision tree classifier achieves F1-score and AUC of 0.91 and 0.93, respectively.