Length-of-Stay prediction
20 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Length-of-Stay prediction
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MIMIC-III
Clinical Admission Notes from MIMIC-III
Most implemented papers
Interpolation-Prediction Networks for Irregularly Sampled Time Series
mlds-lab/interp-net
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ICLR 2019
The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network.
Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks
EmmaRocheteau/eICU-LoS-prediction
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The pressure of ever-increasing patient demand and budget restrictions make hospital bed management a daily challenge for clinical staff.
Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit
EmmaRocheteau/eICU-LoS-prediction
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In this work, we propose a new deep learning model based on the combination of temporal convolution and pointwise (1x1) convolution, to solve the length of stay prediction task on the eICU and MIMIC-IV critical care datasets.
Predicting Patient Outcomes with Graph Representation Learning
EmmaRocheteau/eICU-GNN-LSTM
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Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications.
X-CAL: Explicit Calibration for Survival Analysis
rajesh-lab/X-CAL
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NeurIPS 2020
A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals.
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Learning from a Biased Sample
Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling.
Hospitalization Length of Stay Prediction using Patient Event Sequences
Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations.
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
In this work, we address the step prior to the initiation of a federated network for model training, client recruitment.
Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction
This paper uses the MIMIC-IV dataset to examine the fairness and bias in an XGBoost binary classification model predicting the Intensive Care Unit (ICU) length of stay (LOS).
