medical

Mortality Prediction

3260 papers • 126 benchmarks • 313 datasets

( Image credit: Early hospital mortality prediction using vital signals )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in mortality-prediction-1

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Dataset

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MIMIC-III

Clinical Admission Notes from MIMIC-III

Libraries

i

Use these libraries to find mortality-prediction-1 models and implementations

dmitrykazhdan/MEME-RNN-XAI

3 papers 12

Datasets

MIMIC-III

MeDAL

eICU-CRD

Clinical Admission Notes from MIMIC-III

Subtasks

ICU Mortality

Most implemented papers

Multitask learning and benchmarking with clinical time series data

A. Galstyan, Hrant Khachatrian, Hrayr Harutyunyan, David C. Kale•Tue Mar 21 2017

This work proposes four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database, covering a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification.

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MIMIC-III, a freely accessible critical care database

Alistair E. W. Johnson, T. Pollard, R. Mark, Lu Shen, Li-wei H. Lehman, M. Feng, M. Ghassemi, Benjamin Moody, Peter Szolovits, L. Celi•Sat Apr 30 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. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Design Type(s) data integration objective Measurement Type(s) Demographics • clinical measurement • intervention • Billing • Medical History Dictionary • Pharmacotherapy • clinical laboratory test • medical data Technology Type(s) Electronic Medical Record • Medical Record • Electronic Billing System • Medical Coding Process Document • Free Text Format Factor Type(s) Sample Characteristic(s) Homo sapiens Design Type(s) data integration objective Measurement Type(s) Demographics • clinical measurement • intervention • Billing • Medical History Dictionary • Pharmacotherapy • clinical laboratory test • medical data Technology Type(s) Electronic Medical Record • Medical Record • Electronic Billing System • Medical Coding Process Document • Free Text Format Factor Type(s) Sample Characteristic(s) Homo sapiens Machine-accessible metadata file describing the reported data (ISA-Tab format)

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Efficient nonparametric statistical inference on population feature importance using Shapley values

B. Williamson, Jean Feng•Mon Jun 15 2020

This work presents a computationally efficient procedure for estimating and obtaining valid statistical inference on the Shapley Population Variable Importance Measure (SPVIM), and proves that its estimator converges at an asymptotically optimal rate.

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A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

Yinghao Zhu, Liantao Ma, Wen Tang, Yasha Wang, Ewen M. Harrison, Junyi Gao, Wenqing Wang•Thu Sep 15 2022

Two new prediction tasks are introduced—outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care—which better reflect clinical realities and contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling.

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Using Clinical Notes with Time Series Data for ICU Management

Shafiq R. Joty, Swaraj Khadanga, Karan Aggarwal, J. Srivastava•Wed Sep 11 2019

This work shows that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management.

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Neural Document Embeddings for Intensive Care Patient Mortality Prediction

Stephanie L. Hyland, Paulina Grnarova, Florian Schmidt, Carsten Eickhoff•Wed Nov 30 2016

A convolutional document embedding approach based on the unstructured textual content of clinical notes shows significant performance gains compared to previously employed methods such as latent topic distributions or generic doc2vec embeddings for post-discharge mortality prediction.

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Benchmarking machine learning models on multi-centre eICU critical care dataset

Seyedmostafa Sheikhalishahi, Vevake Balaraman, V. Osmani•Tue Oct 01 2019

This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with the authors' predictive model and investigating the impact of numerical variables as well as handling of categorical variables on each of the defined tasks.

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Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

Chuizheng Meng, Yan Liu, Zhengping Che, S. Purushotham•Sun Oct 22 2017

This paper benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction.

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Early Hospital Mortality Prediction using Vital Signals

W. Romine, Reza Sadeghi, Tanvi Banerjee•Sat Mar 17 2018

It is indicated that heart rate signals can be used for predicting mortality in patients in the care units especially coronary care units (CCUs), achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.

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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

A. Ercole, José Miguel Hernández-Lobato, Yingzhen Li, Yordan Zaykov, Cheng Zhang, Anna-Lena Popkes, H. Overweg•Mon May 06 2019

This work proposes a novel interpretable Bayesian neural network architecture which offers both the flexibility of ANNs and interpretability in terms of feature selection, and employs a sparsity inducing prior distribution in a tied manner to learn which features are important for outcome prediction.

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