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…
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.
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.
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…
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.
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.
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.
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.
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…
New scalable method balances hospital profit status and heart attack outcomes.
problem Balancing covariate distributions and minimizing weight dispersion in large datasets.
method Combines kernel basis expansion and convex optimization for efficient and flexible weighting.
result For-profit hospitals use interventional cardiology similarly to other hospitals but have higher mortality and readmission rates.
Study develops a predictive model to reduce hospital readmissions.
problem Inaccurate readmission risk prediction models in clinical settings.
method Used Genetic Algorithm and Greedy Ensemble to optimize a readmission risk prediction model.
result Developed a useful risk prediction model for reducing unplanned readmissions.
Study compares ARIMA, ETS, NNAR and hybrids for COVID-19 hospitalizations.
problem Forecasting the second wave of COVID-19 hospitalizations in Italy.
method ARIMA, ETS, NNAR, and hybrid models.
result Hybrid models outperform single models in capturing epidemic patterns.
A novel continual prediction model outperforms traditional one-time models in predicting AKI.
problem Optimally predicting AKI before it develops during a hospital stay.
method A novel continual prediction model that predicts AKI every time a patient's AKI-relevant variable changes in the EHR.
result The continual prediction model outperformed traditional one-time models, achieving a higher AUC of 0.724 compared to 0.653.
Study uses ML to predict cancer patient mortality from FN onset.
problem Predicting mortality in cancer patients with FN to improve survival.
method Multi-domain machine learning models using HCUP data.
result Clinical diagnoses have highest predictive power for FN mortality.
This work proposes a student-teacher network for predicting hospital admission locations.
problem Accurate prediction of hospital admission locations to optimize resource allocation.
method Reinforcement learning approach where a teacher network selects data batches for a student network.
result The approach outperforms state-of-the-art methods on tabular data and image recognition.
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.
Machine learning models trained on hospital data degrade over time due to changing practices.
problem Machine learning models trained on hospital data degrade over time due to changing practices.
method We augmented MIMIC with the year of care and showed that a model trained using standard feature representations will significantly degrade in quality over time.
result Clinically-oriented aggregates of raw features significantly mitigate future deterioration.
Bayesian model forecasts hospital resource use during pandemic.
problem Modeling constrained hospital resources during pandemic.
method Approximate Bayesian Computation for an ACED-HMM.
result Mechanistic approach provides competitive probabilistic forecasts.
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
problem Evaluating the impact of algorithmic policy decisions.
method Developed a treatment-effect estimator using algorithmic decisions as instruments.
result Funding from an algorithmic relief rule had little effect on COVID-19-related hospital activities.
New method for k-modes algorithm improves clustering performance.
problem Improving initial solution selection for k-modes algorithm.
method Uses Hospital-Resident Assignment Problem to find initial cluster centroids.
result Outperforms other initialisations in most cases, especially for low-density data.
The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients througho…
Unified ML approach predicts ED attendances with high accuracy.
problem Managing hospital demand at emergency departments efficiently.
method Ensemble of time series and machine learning approaches with hyperparameter tuning.
result Predictions with mean absolute error of +/- 14 and +/- 10 patients, MAE of 6.8% and 8.6%.
The paper shows how the timing of prediction impacts model performance in healthcare.
problem The timing of prediction affects model performance in healthcare.
method The paper compares two prediction schemes: outcome-dependent and outcome-independent.
result An outcome-independent scheme outperforms an outcome-dependent scheme.
Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine…
Study evaluates cross-validation methods for clinical ECG classification, finding leave-source-out more reliable.
problem Overoptimistic cross-validation estimates for new patient sources.
method Empirical evaluation of K-fold and leave-source-out cross-validation methods.
result Leave-source-out cross-validation provides more reliable performance estimates.
Tree-based model averaging improves CATE estimation from diverse sites.
problem Limited sample size and privacy concerns prevent accurate personalized treatment effect estimation.
method Tree-based model averaging approach to estimate CATEs from multiple heterogeneous sites.
result Improved accuracy in estimating conditional average treatment effects (CATEs) across sites.
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
problem Accurate prediction of all-cause in-hospital mortality in healthcare.
method Comprehensive feature engineering approach using vital signs, laboratory results, and demographic data.
result Random Forest model achieved highest performance with AUC of 0.94, significantly outperforming other models.
Study uses data to analyze COPD patients' impact on hospital systems.
problem Understanding and quantifying resource requirements for COPD patients.
method Combines segmentation, queuing theory, and data recovery techniques.
result Finding useful operational results from incomplete administrative data.
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.
Federated learning clusters EMRs to improve mortality and stay predictions.
problem Decentralized, non-IID EMRs complicate centralized machine learning algorithms.
method Introduced CBFL algorithm that clusters EMRs into communities for federated learning.
result CBFL outperforms baseline FL algorithm in ROC AUC, PR AUC, and communication cost.
Machine learning predicts one-year mortality with high accuracy.
problem Predicting one-year mortality for patients with life-limiting illnesses.
method Applied five machine learning techniques: SVM, KNN, Gradient Boosting, Random Forest, and MLP.
result Gradient Boosting Classifier achieved the highest AUC ROC of 0.911.
Bayesian optimization improves classifier selection for acute infection and mortality.
problem Improving accuracy of acute infection and mortality prediction.
method Comparison of hyperparameter optimization methods (grid search, random sampling, Bayesian optimization).
result Bayesian optimization outperforms grid search or random sampling for in-hospital mortality classifiers.
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.
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.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of E…
New approach learns missing data representations for better patient outcome predictions.
problem Missing data, diverse modalities, and complex relationships in EHRs.
method Representation learning using message passing.
result Competitive or superior performance in predicting patient outcomes.