New model predicts ICU patient stays more accurately.
problem Efficient ICU bed allocation under resource constraints.
method Temporal Pointwise Convolutional Networks (TPC) combining temporal and pointwise convolutions.
result Significant performance improvements over LSTM and Transformer models.
New model predicts ICU patients' stay duration efficiently.
problem Efficient ICU bed allocation under resource constraints.
method Temporal Pointwise Convolution (TPC) model combining temporal and pointwise convolutions.
result Significant performance improvements over LSTM and Transformer models.
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to …
Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures include zero inflation, over-dispersion, and skewness, all of which complicate st…
Selective prediction-set models improve predictive reliability and uncertainty quantification.
problem Inaccurate and unreliable predictions from black-box models, especially for unfamiliar data.
method Training selective prediction-set models using uncertainty-aware loss minimization, and calculating well-calibrated prediction sets.
result Selective prediction-set models outperform existing approaches in predicting in-hospital mortality and length-of-stay for ICU patients.
A novel approach combines interpretability and performance in machine learning models.
problem Lack of transparency in black box machine learning models.
method Semiparametric approach using ideas from sufficient dimension reduction and influence function based estimators.
result Optimized model combining interpretability and performance, demonstrated through simulations and a real-world ICU patient data application.
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.
Proposes proactive bed requests to reduce ED boarding and patient wait times.
problem Reduces ED boarding and patient wait times by proactively requesting inpatient beds.
method Formulates as a Markov decision process, uses predictions of admission probability and time to disposition to guide bed requests.
result Proactive aggregate bed requests can reduce boarding times by 30-70% and length of stay by 6-15%.
While the volume of electronic health records (EHR) data continues to grow, it remains rare for hospital systems to capture dense physiological data streams, even in the data-rich intensive care unit setting. Instead, typical EHR records consist of sparse and irregularly observed multivariate time series, which are wel…
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absen…
Stratifying patients at risk for postoperative complications may facilitate timely and accurate workups and reduce the burden of adverse events on patients and the health system. Currently, a widely-used surgical risk calculator created by the American College of Surgeons, NSQIP, uses 21 preoperative covariates to asse…
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.
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do…
Study automates detection of visitation disruptions in ICU patients.
problem Difficulty in detecting frequent visitation disruptions in ICU patients.
method Used DensePose R-CNN model to count people in video frames, analyzed disruptions and patient outcomes.
result Automated method detects visitation disruptions, impacts on pain and length of stay examined.
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.
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with…
Clinical notes improve ICU patient condition prediction.
problem Predicting ICU patient outcomes and resource planning.
method Joint modeling of time series data and clinical notes.
result Significant improvement in mortality prediction, decompensation modeling, and length of stay forecasting.
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.
Public benchmark for machine learning models in critical care.
problem Lack of public benchmarks for machine learning in critical care.
method Defined four tasks (mortality prediction, length of stay, phenotyping, decompensation risk) and compared clinical and deep learning models on eICU dataset.
result First public benchmark on multi-centre critical care dataset, comparing clinical models with predictive models.
Study finds little progress in medical machine learning benchmarks over 3 years.
problem Lack of meaningful progress in medical machine learning benchmarks for structured healthcare data.
method Comprehensive review and meta-analysis of benchmarks in medical machine learning for structured data.
result Deep recurrent models perform only better than logistic regression on certain clinical prediction tasks.
A new model improves homogeneity in burn patient reimbursement.
problem Incomplete homogeneity checks for burn patients using LOS as a proxy.
method Cost-sensitive decision tree model considering patient-level cost and severity of burn.
result Identified groups with increased homogeneity compared to current HRG groups.
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.
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.
The paper tackles distribution-free prediction intervals for multi-source data.
problem Challenges in achieving valid inferences due to distribution shifts and privacy concerns.
method Derives efficient influence functions, incorporates machine learning, and proposes data-adaptive strategies.
result Achieves parametric rates of convergence to nominal coverage probabilities for prediction intervals.
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…
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.
Study evaluates approaches to improve worst-case model performance across patient subpopulations.
problem Improving model accuracy for specific patient subpopulations.
method Comparison of distributionally robust optimization (DRO) and standard learning procedures.
result Standard learning procedures generally outperform DRO approaches for improving model performance across subpopulations.
Proposes methods to learn from biased samples, ensuring robust decision rules.
problem Learning from biased samples can lead to poor performance in real-world applications.
method Modeling sampling bias, using distributionally robust optimization and deep learning.
result Proposes a method to minimize worst-case risk under various test distributions.
CovidCare uses EMR data to predict patient outcomes in emerging epidemics.
problem Intelligent prognosis for patients with emerging infectious diseases during rapid epidemics.
method Transfer learning and knowledge distillation from existing EMR data.
result CovidCare outperforms baseline methods in predicting patient length of stay.
Neural model predicts survival outcomes and reveals feature relationships.
problem Predicting time-to-event outcomes and understanding feature relationships in clinical data.
method Survival and topic modeling combined in a neural network framework.
result Neural survival-supervised topic models achieve competitive accuracy with interpretability.
Robust policies improve ICU transfer outcomes by predicting patient deterioration.
problem Higher mortality rates for unplanned ICU transfers.
method Markov Decision Process model to predict patient severity and optimize transfer policies.
result Robust policies are more aggressive in transferring patients than nominal policies, improving overall patient care.