Predicts hospital patient discharge within 24 hours to optimize resource allocation.
problem Improving hospital resource management and patient care by prioritizing discharge.
method Used eight years of EHR data to train models predicting 24-hour discharge.
result Best models achieved AUROC of 0.85 and AUPRC of 0.53, well calibrated.
CLIP dataset helps extract action items from hospital discharge notes.
problem Lost action items in long discharge notes hinder information sharing.
method Created CLIP dataset of annotated discharge notes, used multi-aspect extractive summarization, trained models on pre-trained language models and context.
result Best models improved by incorporating context and pre-trained language models.
Natural language processing predicts ICU readmissions with 74.8% accuracy.
problem Early detection of ICU readmissions to improve patient outcomes and reduce costs.
method Natural language processing of discharge summaries, machine learning classifiers, UMLS standardization.
result Best configuration achieved an AUC of 0.748 for predicting ICU readmissions.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic informatio…
The paper improves SVM for predicting hospital readmissions by clustering patients and personalizing recommendations.
problem Predicting and preventing hospital readmissions.
method Cluster-dependent SVM with personalized prescriptions.
result Personalized recommendations reduce hospital readmissions.
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that …
Deep learning predicts 30-day readmissions after CABG surgery.
problem Predicting 30-day readmissions after CABG surgery.
method Ensembled model using machine learning survival analysis techniques.
result DeepSurv model outperformed traditional Cox Proportional Hazard model.
Study re-evaluates MIMIC-III codes, finding many are under-coded.
problem Validity of MIMIC-III clinical codes is questionable.
method Open-source, reproducible methodology for assessing codes.
result Most frequently assigned codes are under-coded up to 35%
New method for discrete-time survival analysis with competing risks.
problem Discrete failure time data in survival analysis.
method Proposes a new estimation procedure for discrete-time survival analysis with competing events.
result Offers advantages over existing procedures and integrates regularized regression methods.
AI generates a sequence of death causes from hospital records.
problem Accurate death reporting for vital statistics and policy formulation.
method Neural machine translation models to generate causal chains, incorporating medical domain knowledge.
result Achieved 16.04 BLEU score for generating accurate causal chains.
New method detects spike-and-wave epileptiform discharges using Kendall's Tau-b.
problem Detecting spike-and-wave epileptiform discharges in EEG signals.
method Proposes a new method based on Kendall's Tau-b coefficient.
result High Specificity and rule in (SpPIn) for spike-and-wave discharge detection.
Estimates river discharge using few measurements at many locations.
problem Accurate river discharge estimation at scale using scarce in-situ measurements.
method Developed a common mechanism regression (CMR) model with local and shared components.
result Found global optimum for non-convex learning objective using spectral initialization and descent methods.
Deep learning improves probabilistic river discharge forecasting for hydroelectric power.
problem Uncertain river discharges due to climate variability.
method Modified recurrent neural network architecture conditioned on global circulation model projections.
result Generates parameterized probability distributions for realistic long-term discharge scenarios.
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform members…
Neural networks outperform logistic regression for predicting HF readmission.
problem Predicting 30-day all-cause readmission in heart failure patients.
method Used a large administrative claims dataset to compare neural network models (RNNCRF) with logistic regression models (LASSO) for predicting readmission.
result RNNCRF model achieved best performance with 0.642 AUC, while logistic regression with LASSO had equal performance.
Study identifies risk factors for subsequent suicide attempts in youth.
problem Uncertainty in suicide attempt identification from medical claims data.
method Integrative Cox cure model with regularization for survival analysis with uncertain events.
result Identifies risk factors for subsequent suicide attempts and distinguishes susceptibility from timing.
The paper tackles ICU discharge strategies by evaluating optimal stopping scenarios.
problem Optimal stopping in ICU discharge with complex causal inference challenges.
method Generalized g-formula implementation and application to MIMIC-IV dataset.
result Improved ICU discharge strategies demonstrated with the g-formula approach.
Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effect…
LSTM models with DI enhance streamflow forecasts across diverse regions.
problem Challenges in integrating varied discharge measurements for accurate streamflow forecasts.
method Flexible data integration (DI) using LSTM models with CNN units for lagged inputs.
result DI significantly improved streamflow forecast performance, reaching record efficiency coefficients.
Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge …
Bayesian models forecast COVID-19 hospitalizations at single sites.
problem Forecasting daily COVID-19 hospitalizations at a single hospital.
method Hierarchical Bayesian models with generalized Poisson likelihood and autoregressive/Gaussian process latent processes.
result Demonstrated superior performance compared to baselines in public datasets.
Study predicts future hospitalizations to manage COVID-19 patient surge.
problem Managing hospitalization needs during COVID-19 pandemic.
method Used 4 recurrent neural networks to predict hospitalization changes.
result Sequence to sequence model with attention achieved high accuracy and AUC.
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis i…
X-CAL improves survival model calibration without sacrificing predictive power.
problem Improving the calibration of survival models to better match observed data.
method Explicit calibration (X-CAL) turns distributional calibration into a differentiable objective for survival modeling.
result X-CAL improves calibration metrics without significantly reducing predictive performance.
Natural language processing improves COVID-19 hospitalization identification.
problem Identifying patients hospitalized due to COVID-19 among those with positive SARS-CoV-2 tests.
method Used natural language processing on provider notes and structured EHR data elements to create classification algorithms.
result Classification algorithms using provider notes outperformed those using only structured EHR data elements, with AUROC of 0.894 compared to 0.841.
Deep learning predicts heart failure readmission from clinical notes.
problem Predicting and preventing heart failure readmission.
method Convolutional Neural Networks (CNN) trained on clinical notes.
result Deep learning models outperform traditional machine learning methods in readmission prediction.
Adversarial method improves pneumonia classifier's performance across hospitals.
problem Robust classification of pneumonia from chest radiographs across different hospitals.
method Adversarial optimization to learn models invariant to confounders.
result Improved out-of-hospital generalization performance compared to baselines.
Graph neural network improves SOH estimation of lithium-ion batteries.
problem Accurate SOH estimation requires alignment of statistical distributions between training and testing datasets.
method Graph convolutional networks (GCNs) with anomaly detection for selecting discharge voltage segments.
result Achieves precise SOH estimation with a root mean squared error of less than 1%.
Deep neural network predicts diabetic readmission with high accuracy.
problem Predicting 30-day readmission for diabetic patients.
method Categorical embeddings and deep neural network.
result 95.2% accuracy and 97.4% AUROC on diabetic readmission data.
CPAS uses machine learning to plan hospital resources for COVID-19.
problem Forecasting hospital resource demands during the COVID-19 pandemic.
method Combining machine learning algorithms with diverse data sources.
result CPAS successfully managed hospital resource planning in the UK.
Study evaluates predictive models across multiple hospitals.
problem Limited data sharing hinders model performance evaluation.
method Cross-validation using eICU Collaborative Research Database.
result Models trained on multi-center data generalize well to new hospitals.
Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the…
CausalRivers benchmarks causal discovery methods on real-world river discharge data.
problem Lack of in-the-wild evaluation of causal discovery methods on complex, real-world data.
method Introduces CausalRivers, a large-scale dataset of river discharge data for benchmarking.
result Demonstrates the utility of CausalRivers in evaluating causal discovery methods.
The paper uses SHAP for interpreting machine learning models in hospital data.
problem Interpreting machine learning models in healthcare.
method SHAP for feature importance and feature packing techniques.
result SHAP provides better interpretability of machine learning models in healthcare.
FUALA improves Federated Learning for EHR data, enhancing model uncertainty.
problem Applying ML to EHR data while maintaining privacy and accuracy.
method FUALA embeds uncertainty in federated learning, using ensembling and averaging.
result FUALA outperforms FedAvg on out-of-distribution data in EHR model predictions.
We investigate the automatic classification of patient discharge notes into standard disease labels. We find that Convolutional Neural Networks with Attention outperform previous algorithms used in this task, and suggest further areas for improvement.
Privacy-preserving continual learning for hospitals.
problem Forgetting old data when updating neural networks for privacy.
method Differentially private variational inference and generative models.
result Estimates likelihood of old data given current model.
Algorithm detects surgical site infections from hospital data.
problem Reduces incidence of surgical site infections in hospitals.
method Machine learning algorithms trained on hospital data.
result Identifies all SSIs with minimal false positives.
Categorical Co-Frequency Analysis clusters diagnoses to predict hospital readmissions.
problem Predicting patients' risk of 30-day hospital readmission.
method Categorical Co-Frequency Analysis (CoFA) measures diagnosis similarity using random forests.
result Identified three groups of diagnoses with varying readmission risk.
Paper shows federated learning can train models on private data.
problem Training predictive models on private hospital data.
method Distributed training in federated learning framework.
result Federated learning achieves comparable performance to traditional methods.
Machine learning improves early detection of patient deterioration in Brazilian hospitals.
problem Challenges in recognizing clinical deterioration in hospital settings.
method Application of machine learning to analyze EHR data from multiple hospitals.
result Machine learning models outperformed traditional protocols by 25 percentage points in AUC.
Deep network clusters hospital patients' vital signs.
problem Sparse and irregularly collected vital sign data.
method Deep interpolation network for latent representation extraction.
result Extracted 7 distinct clusters from vital sign data.
Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.
problem Quantifying uncertainty in a multi-component Hall thruster model at different facility pressures.
method Bayesian inference applied to calibrate and quantify prediction uncertainty in a coupled multi-component Hall thruster model.
result Model reduces predictive errors in thrust and discharge current by more than 50% compared to a previous model.
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.
Hospital readmissions have become one of the key measures of healthcare quality. Preventable readmissions have been identified as one of the primary targets for reducing costs and improving healthcare delivery. However, most data driven studies for understanding readmissions have produced black box classification and p…