medical-3

Respiratory Failure

3260 papers • 126 benchmarks • 313 datasets

Continuous prediction of onset of respiratory failure in the next 12h given the patient is not in failure now.

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Most implemented papers

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series

Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, M. Sjoding, J. Wiens•Mon Dec 31 2018

A novel RNN formulation based on a mixture model in which relaxed parameter sharing over time is proposed, which outperforms standard LSTMs and other state-of-the-art baselines across all tasks and can lead to improved patient risk stratification performance.

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A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room

M. Avidan, Zhicheng Cui, Yixin Chen, Bradley A. Fritz, C. King•Sun Jun 30 2019

A factored generalized additive model (F-GAM) is proposed to preserve the model interpretability for targeted features while allowing a rich model for interaction with features fixed within the individual.

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How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series

Zsigmond Benkő, Tamás Bábel, Zoltán Somogyvári•Wed Apr 22 2020

The algorithm successfully recognized unique events in those cases where they were already known such as the gravitational waves of a black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series.

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Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure

M. Sjoding, J. Wiens, Sarah Jabbour, David Fouhey, E. Kazerooni•Thu Aug 26 2021

Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF, and may act as a diagnostic aid to clinicians in clinical settings.

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Temporal Label Smoothing for Early Event Prediction

Hugo Yèche, Rita Kuznetsova, Alizée Pace, G. Ratsch•Sun Aug 28 2022

TLS is proposed, a simpler, yet best-performing method that preserves prediction monotonicity over time and reduces the number of missed events by up to a factor of two over previously used approaches in early event prediction.

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Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise

J. Wiens, Donna Tjandra•Sun Jul 09 2023

The approach improves accuracy while mitigating potential bias compared to existing approaches in the presence of instance-dependent label noise, and leads to consistent improvements over the state-of-the-art in discriminative performance (AUROC) while mitigating bias (area under the equalized odds curve, AUEOC).

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