Vital Sign Forecasting for Sepsis Patients in ICUs

• URL: http://arxiv.org/abs/2311.04770v1
• Date: Wed, 8 Nov 2023 15:47:58 GMT
• Title: Vital Sign Forecasting for Sepsis Patients in ICUs
• Authors: Anubhav Bhatti, Yuwei Liu, Chen Dan, Bingjie Shen, San Lee, Yonghwan Kim, Jang Yong Kim
• Abstract summary: This paper uses state-of-the-art deep learning (DL) architectures to introduce a multi-step forecasting system. We introduce a DL-based vital sign forecasting system that predicts up to 3 hours of future vital signs from 6 hours of past data. We evaluate the performance of our models using mean squared error (MSE) and dynamic time warping (DTW) metrics.
• Score: 5.543372375499915
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sepsis and septic shock are a critical medical condition affecting millions globally, with a substantial mortality rate. This paper uses state-of-the-art deep learning (DL) architectures to introduce a multi-step forecasting system to predict vital signs indicative of septic shock progression in Intensive Care Units (ICUs). Our approach utilizes a short window of historical vital sign data to forecast future physiological conditions. We introduce a DL-based vital sign forecasting system that predicts up to 3 hours of future vital signs from 6 hours of past data. We further adopt the DILATE loss function to capture better the shape and temporal dynamics of vital signs, which are critical for clinical decision-making. We compare three DL models, N-BEATS, N-HiTS, and Temporal Fusion Transformer (TFT), using the publicly available eICU Collaborative Research Database (eICU-CRD), highlighting their forecasting capabilities in a critical care setting. We evaluate the performance of our models using mean squared error (MSE) and dynamic time warping (DTW) metrics. Our findings show that while TFT excels in capturing overall trends, N-HiTS is superior in retaining short-term fluctuations within a predefined range. This paper demonstrates the potential of deep learning in transforming the monitoring systems in ICUs, potentially leading to significant improvements in patient care and outcomes by accurately forecasting vital signs to assist healthcare providers in detecting early signs of physiological instability and anticipating septic shock.

Related papers

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• 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)
• Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps [5.354055742467353]
  This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical time steps in the forecasting process. We show that the attention mechanism could be adapted to various black box time series forecasting models such as N-HiTS and N-BEATS.
  arXiv  Detail & Related papers  (2024-05-02T20:19:07Z)
• A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series [2.621671379723151]
  We use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model. We train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction.
  arXiv  Detail & Related papers  (2023-11-16T05:06:51Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients [0.5541644538483947]
  Our research examines N-BEATS, an interpretable deep-learning forecasting model that can forecast 3 hours of vital signs for sepsis patients in intensive care units (ICUs) We use the N-BEATS interpretable configuration to forecast the vital sign trends and compare them with the actual trend to understand better the patient's changing condition and the effects of infused drugs on their vital signs. We observed that the mortality rate was higher (92%) when the actual and forecasted trends closely matched, compared to when they were not similar.
  arXiv  Detail & Related papers  (2023-06-24T16:23:54Z)
• 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)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction [21.919977518774015]
  We study how physiological time series data and clinical notes can be integrated into a unified mortality prediction model. Our results show that a late fusion approach can provide a statistically significant improvement in prediction mortality over using individual modalities in isolation.
  arXiv  Detail & Related papers  (2020-03-24T18:25:49Z)
• Forecasting adverse surgical events using self-supervised transfer learning for physiological signals [7.262231066394781]
  We present a transferable embedding method (i.e., a method to transform time series signals into input features for predictive machine learning models) named PHASE. We evaluate PHASE on minute-by-minute EHR data of more than 50,000 surgeries from two operating room (OR) datasets and patient stays in an intensive care unit (ICU) dataset. In a transfer learning setting where we train embedding models in one dataset then embed signals and predict adverse events in unseen data, PHASE achieves significantly higher prediction accuracy at lower computational cost compared to conventional approaches.
  arXiv  Detail & Related papers  (2020-02-12T02:49:15Z)

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.