Length-of-Stay prediction
19 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Length-of-Stay prediction
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MIMIC-III
Clinical Admission Notes from MIMIC-III
Most implemented papers
Multitask learning and benchmarking with clinical time series data
yerevann/mimic3-benchmarks
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Health care is one of the most exciting frontiers in data mining and machine learning.
MIMIC-III, a freely accessible critical care database
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
chantalmp/unsupervised-pre-training-on-patient-population-graphs-for-patient-level-predictions
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We test our method on two medical datasets of patient records, TADPOLE and MIMIC-III, including imaging and non-imaging features and different prediction tasks.
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
yhzhu99/covid-ehr-benchmarks
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Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data.
Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics
To what extent can the patient's length of stay in a hospital be predicted using only an X-ray image?
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
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.
Modeling Irregularly Sampled Clinical Time Series
In this paper, we present a new deep learning architecture for addressing this problem based on the use of a semi-parametric interpolation network followed by the application of a prediction network.
Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data
yerevann/mimic3-benchmarks
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Nature Scientific Data 2019
Health care is one of the most exciting frontiers in data mining and machine learning.
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
MLforHealth/MIMIC_Generalisation
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When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.
