Self-Supervised Predictive Coding with Multimodal Fusion for Patient
Deterioration Prediction in Fine-grained Time Resolution
- URL: http://arxiv.org/abs/2210.16598v2
- Date: Thu, 13 Apr 2023 06:07:32 GMT
- Title: Self-Supervised Predictive Coding with Multimodal Fusion for Patient
Deterioration Prediction in Fine-grained Time Resolution
- Authors: Kwanhyung Lee, John Won, Heejung Hyun, Sangchul Hahn, Edward Choi,
Joohyung Lee
- Abstract summary: We propose an hourly prediction method based on self-supervised predictive coding and multi-modal fusion for two critical tasks: mortality and vasopressor need prediction.
Through extensive experiments, we prove significant performance gains from both multi-modal fusion and self-supervised predictive regularization.
- Score: 6.806410144139259
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Accurate time prediction of patients' critical events is crucial in urgent
scenarios where timely decision-making is important. Though many studies have
proposed automatic prediction methods using Electronic Health Records (EHR),
their coarse-grained time resolutions limit their practical usage in urgent
environments such as the emergency department (ED) and intensive care unit
(ICU). Therefore, in this study, we propose an hourly prediction method based
on self-supervised predictive coding and multi-modal fusion for two critical
tasks: mortality and vasopressor need prediction. Through extensive
experiments, we prove significant performance gains from both multi-modal
fusion and self-supervised predictive regularization, most notably in
far-future prediction, which becomes especially important in practice. Our
uni-modal/bi-modal/bi-modal self-supervision scored 0.846/0.877/0.897
(0.824/0.855/0.886) and 0.817/0.820/0.858 (0.807/0.81/0.855) with mortality
(far-future mortality) and with vasopressor need (far-future vasopressor need)
prediction data in AUROC, respectively.
Related papers
- SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
Sepsis is the leading cause of in-hospital mortality in the USA.
Existing predictive models are usually trained on high-quality data with few missing information.
For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
arXiv Detail & Related papers (2024-07-24T04:47:36Z) - Boosting the interpretability of clinical risk scores with intervention
predictions [59.22442473992704]
We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions.
We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.
arXiv Detail & Related papers (2022-07-06T19:49:42Z) - Multimodal spatiotemporal graph neural networks for improved prediction
of 30-day all-cause hospital readmission [4.609543591101764]
We propose a multimodal, modality-agnostic graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission.
MM-STGNN achieves AU of 0.79 on both primary and external datasets.
For subset populations of patients with heart and vascular disease, our model also outperforms baselines on predicting 30-day readmission.
arXiv Detail & Related papers (2022-04-14T05:50:07Z) - Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG
Time Series [37.15225732922409]
Motivated by this real-world application, we present BeneFitter that infuses the incurred savings from an early prediction as well as the cost from misclassification into a unified domain-specific target called benefit.
BeneFitter is efficient and fast, with training time linear in the number of input sequences, and can operate in real-time for decision-making.
arXiv Detail & Related papers (2021-11-11T02:54:36Z) - When in Doubt: Neural Non-Parametric Uncertainty Quantification for
Epidemic Forecasting [70.54920804222031]
Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions.
Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations.
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
arXiv Detail & Related papers (2021-06-07T18:31:47Z) - STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological
Regularization [76.57716281104938]
We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously.
STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations.
We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
arXiv Detail & Related papers (2020-12-08T21:21:47Z) - 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) - UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model.
UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data.
We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD)
UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z) - Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials.
We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
arXiv Detail & Related papers (2020-06-14T01:15:00Z) - ISeeU2: Visually Interpretable ICU mortality prediction using deep
learning and free-text medical notes [0.0]
We show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance.
Our model reaches a ROC of 0.8629, outperforming the traditional SAPS-II score and providing enhanced interpretability when compared with similar Deep Learning approaches.
arXiv Detail & Related papers (2020-05-19T08:30:34Z)
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.