Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model

• URL: http://arxiv.org/abs/2502.08612v1
• Date: Wed, 12 Feb 2025 18:01:04 GMT
• Title: Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model
• Authors: Saurabh Kataria, Ran Xiao, Timothy Ruchti, Matthew Clark, Jiaying Lu, Randall J. Lee, Jocelyn Grunwell, Xiao Hu,
• Abstract summary: Non-invasive patient monitoring for tracking and predicting acute health events is an emerging area of research. We present IHCA prediction results in ICU patients using only unimodal (signal waveform) deep representations. We also provide comprehensive analysis of our model through architectural tuning and PaCMAP visualization of patient health trajectory in latent space.
• Score: 6.469423282286416
• License:
• Abstract: Non-invasive patient monitoring for tracking and predicting adverse acute health events is an emerging area of research. We pursue in-hospital cardiac arrest (IHCA) prediction using only single-channel finger photoplethysmography (PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator Network (FEAN) leverages powerful representations from pre-trained PPG foundation models (PPG-GPT of size up to 1 Billion) stacked with sequential classification models. We propose two FEAN variants ("1H", "FH") which use the latest one-hour and (max) 24-hour history to make decisions respectively. Our study is the first to present IHCA prediction results in ICU patients using only unimodal (continuous PPG signal) waveform deep representations. With our best model, we obtain an average of 0.79 AUROC over 24~h prediction window before CA event onset with our model peaking performance at 0.82 one hour before CA. We also provide a comprehensive analysis of our model through architectural tuning and PaCMAP visualization of patient health trajectory in latent space.

Related papers

• PaPaGei: Open Foundation Models for Optical Physiological Signals [8.78925327256804]
  Photoplethysmography is the leading non-invasive technique for monitoring biosignals and cardiovascular health. Machine learning models trained on PPG signals tend to be task-specific and struggle with generalization. We present PaPaGei, the first open foundation model for PPG signals.
  arXiv  Detail & Related papers  (2024-10-27T18:18:06Z)
• 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)
• Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions [48.02011627390706]
  Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. In this paper, we apply unsupervised pre-training to heterogeneous, multi-modal EHR data for patient outcome prediction. We find that our proposed graph based pre-training method helps in modeling the data at a population level.
  arXiv  Detail & Related papers  (2022-03-23T17:59:45Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• An Interpretable Web-based Glioblastoma Multiforme Prognosis Prediction Tool using Random Forest Model [1.1024591739346292]
  We propose predictive models that estimate GBM patients' health status of one-year after treatments. We used total of 467 GBM patients' clinical profile consists of 13 features and two follow-up dates. Our machine learning models suggest that the top three prognostic factors for GBM patient survival were MGMT gene promoter, the extent of resection, and age.
  arXiv  Detail & Related papers  (2021-08-30T07:56:34Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• 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)
• Individualized Prediction of COVID-19 Adverse outcomes with MLHO [9.197411456718708]
  We developed an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health outcomes. We modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. Our results demonstrated that while demographic variables are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model.
  arXiv  Detail & Related papers  (2020-08-10T02:44:52Z)
• A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction [0.7930054475711718]
  The aim of this study was to build computational models to predict post-CA outcome. We hypothesized that model performance could be enhanced by integrating physiological time series (PTS) data and by training machine learning (ML) classifiers. Results attest to the effectiveness of ML models for post-CA predictive modeling and suggest that PTS recorded in very early phase after resuscitation encode short-term outcome probabilities.
  arXiv  Detail & Related papers  (2020-02-09T07:53:50Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

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.