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.
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
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.
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.
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
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.
The paper studies causal effects of multiple treatments in healthcare databases with rare outcomes.
problem Estimating causal effects of multiple treatments in healthcare databases with rare outcomes.
method The paper designs three sets of simulations and compares the operating characteristics of three types of methods: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS), and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models.
result BART and RAMS provide lower bias and mean squared error compared to IPTW methods.
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.
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.
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.
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 proposes a multi-task learning approach to predict multiple diabetes complications.
problem Risk prediction and profiling of diabetes complications for personalized treatment plans.
method Multi-task learning approach with coefficient shrinkage and hierarchical Bayesian framework.
result The proposed method outperforms state-of-the-art models in predicting multiple diabetes complications.
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.
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.
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.
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.
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.
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.
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.
Hi-RES framework extracts medical relations from articles and EHRs.
problem Manual annotation bottleneck in relation extraction.
method Labeling sentences, creating improved negative samples, using pretrained language models, and combining EHR embeddings.
result Significant accuracy increases in relation extraction, up to 0.998 for disorder-location relations.
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.
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.
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,…
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.
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.
Machine learning predicts band gaps for large organic crystals.
problem Predicting band gaps for complex organic crystal structures.
method Released a dataset of 12,500 crystal structures and their band gaps. Trained two state-of-the-art models to achieve a mean absolute error of 0.388 eV.
result Trained models predict band gaps with 13% error for an average gap of 3.05 eV.
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.
Paper improves communication in decentralized federated learning for EHRs.
problem Efficiently learn from large, decentralized EHR databases.
method Fully decentralized federated learning with iterative local updates and reduced communication rounds.
result Significant reduction in communication rounds without compromising solution optimality.
We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is re…
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.
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.
We recover regression coefficients from unlabeled binary outcomes using a surrogate variable.
problem Recovering regression coefficients from unlabeled binary outcomes.
method Fit a least squares LASSO estimator to the subset of the observed data on $(oldsymbol{X}, S)$ restricted to the extreme sets of S, with Y imputed using the surrogacy of S. result Sharp finite sample performance bounds for the estimator, including deterministic deviation bounds and probabilistic guarantees.
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.
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.
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.
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.
CCA features from medical codes predict future surgeries.
problem Predicting future surgeries based on medical codes.
method Canonical correlation analysis applied to sequences of medical codes.
result CCA embeddings capture meaningful relationships among medical codes.
ConvSCCS model detects rare adverse drug reactions from EHRs.
problem Underreporting of adverse drug reactions due to physician reports.
method Conditional Poisson model with convolution and penalized step functions.
result Improves estimation of relative risks in diabetic patients.
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.
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.
For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditionally quote-driven i.e., dealer-driven) rather than order-driven, the market for corporate bonds is st…
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.
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.
This study improves AKI prediction precision using CNN on EHR data.
problem Improving early prediction of AKI in ICU patients.
method Convolutional Neural Network (CNN) on EHR data.
result Best AUROC up to 0.988 on MIMIC-III and 0.936 on eICU data sets.
New model predicts particle precipitation from magnetosphere to ionosphere.
problem Improving prediction of electron particle precipitation from magnetosphere to ionosphere.
method Compilation of new database, use of machine learning (ML) tools, neural network (PrecipNet).
result PrecipNet achieves >50% reduction in errors and better captures dynamic changes.
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.
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.
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.