Length-of-Stay prediction

14 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

Multitask learning and benchmarking with clinical time series data

yerevann/mimic3-benchmarks 22 Mar 2017

Health care is one of the most exciting frontiers in data mining and machine learning.

MIMIC-III, a freely accessible critical care database

mit-lcp/mimic-iii-paper Nature 2016

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

MLforHealth/MIMIC_Extract 19 Jul 2019

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.

Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

USC-Melady/Benchmarking_DL_MIMICIII 23 Oct 2017

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

mlds-lab/interp-net 3 Dec 2018

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.

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

MLforHealth/MIMIC_Generalisation 2 Aug 2019

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.

Interpolation-Prediction Networks for Irregularly Sampled Time Series

mlds-lab/interp-net 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 29 Jun 2020

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 18 Jul 2020

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.