Early ICU Mortality Prediction and Survival Analysis for Respiratory
Failure
- URL: http://arxiv.org/abs/2109.03048v1
- Date: Mon, 6 Sep 2021 06:03:23 GMT
- Title: Early ICU Mortality Prediction and Survival Analysis for Respiratory
Failure
- Authors: Yilin Yin and Chun-An Chou
- Abstract summary: We propose a dynamic modeling approach for early mortality risk prediction of the respiratory failure patients based on the first 24 hours ICU physiological data.
We achieved a high AUROC performance (80-83%) and significantly improved AUCPR 4% on Day 5 since ICU admission, compared to the state-of-art prediction models.
- Score: 4.229085609275446
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Respiratory failure is the one of major causes of death in critical care
unit. During the outbreak of COVID-19, critical care units experienced an
extreme shortage of mechanical ventilation because of respiratory failure
related syndromes. To help this, the early mortality risk prediction in
patients who suffer respiratory failure can provide timely support for clinical
treatment and resource management. In the study, we propose a dynamic modeling
approach for early mortality risk prediction of the respiratory failure
patients based on the first 24 hours ICU physiological data. Our proposed model
is validated on the eICU collaborate database. We achieved a high AUROC
performance (80-83%) and significantly improved AUCPR 4% on Day 5 since ICU
admission, compared to the state-of-art prediction models. In addition, we
illustrated that the survival curve includes the time-varying information for
the early ICU admission survival analysis.
Related papers
- Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models.
Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
arXiv Detail & Related papers (2023-10-24T02:56:05Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic
Prediction of Mortality in the ICU for Heart Attack Patients [3.5475382876263915]
Heart attack is one of the greatest contributors to mortality in the United States and globally.
We develop a novel pseudo-dynamic machine learning framework for mortality prediction in the ICU with interpretability and clinical risk analysis.
arXiv Detail & Related papers (2023-05-10T12:53:18Z) - Early prediction of the risk of ICU mortality with Deep Federated
Learning [0.0]
We evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage.
We show that the prediction performance is higher when the patient history window is closer to discharge or death.
arXiv Detail & Related papers (2022-12-01T15:01:27Z) - SurvLatent ODE : A Neural ODE based time-to-event model with competing
risks for longitudinal data improves cancer-associated Deep Vein Thrombosis
(DVT) prediction [68.8204255655161]
We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data.
Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function.
SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
arXiv Detail & Related papers (2022-04-20T17:28:08Z) - Predicting Patient Readmission Risk from Medical Text via Knowledge
Graph Enhanced Multiview Graph Convolution [67.72545656557858]
We propose a new method that uses medical text of Electronic Health Records for prediction.
We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph.
Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
arXiv Detail & Related papers (2021-12-19T01:45:57Z) - Early prediction of respiratory failure in the intensive care unit [1.8312530927511608]
Early prediction of respiratory system failure could alert clinicians to patients at risk of respiratory failure.
We propose an early warning system that predicts moderate/severe respiratory failure up to 8 hours in advance.
arXiv Detail & Related papers (2021-05-12T15:20:09Z) - COVID-19 Prognosis via Self-Supervised Representation Learning and
Multi-Image Prediction [32.91440827855392]
We consider the task of predicting two types of patient deterioration based on chest X-rays.
Due to the relative scarcity of COVID-19 patient data, existing solutions leverage supervised pretraining on related non-COVID images.
In this paper, we use self-supervised learning based on the momentum contrast (MoCo) method in the pretraining phase to learn more general image representations to use for downstream tasks.
arXiv Detail & Related papers (2021-01-13T07:03:17Z) - A Knowledge Distillation Ensemble Framework for Predicting Short and
Long-term Hospitalisation Outcomes from Electronic Health Records Data [5.844828229178025]
Existing outcome prediction models suffer from a low recall of infrequent positive outcomes.
We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission.
arXiv Detail & Related papers (2020-11-18T15:56:28Z) - Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission.
Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
arXiv Detail & Related papers (2020-07-20T19:08:50Z) - Prediction of the onset of cardiovascular diseases from electronic
health records using multi-task gated recurrent units [51.14334174570822]
We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records.
The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
arXiv Detail & Related papers (2020-07-16T17:43:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.