Arterial Blood Gas (ABG) Prediction

Overview

Arterial Blood Gas (ABG) Prediction refers to the process of estimating a patient’s arterial blood gas parameters using clinical data and computational models. ABG analysis is critical for assessing acid–base balance, oxygenation, and v entilation status in critically ill patients. Predictive systems aim to reduce invasive sampling, save time, and support early clinical decision- making.

Purpose

  • Reduce the need for frequent arterial punctures
  • Enable early detection of acid–base disorders
  • Support clinicians in ICU and emergency care settings
  • Assist AI-driven clinical decision support systems

Scope

  • ABG parameters
  • Physiological significance
  • Prediction methodology
  • Data requirements
  • System workflow
  • Applications and limitations

Traditional ABG Analysis

  • Requires invasive arterial sampling
  • Time-consuming process
  • Painful and uncomfortable for patients

ABG Prediction Approach

AI-Based ABG Prediction

  • Predicts ABG values using non-invasive or minimally invasive data

Prediction Targets

  • pH
  • PaCO₂
  • PaO₂
  • HCO₃⁻
  • Lactate

System Workflow

  • Patient data acquisition
  • Data preprocessing and normalization
  • Feature selection
  • ABG parameter prediction
  • Result interpretation and alert generation

Applications

  • Intensive Care Units (ICU)
  • Emergency departments
  • Remote patient monitoring
  • Clinical decision support systems
  • Medical research and training

Advantages

  • Reduces invasive procedures
  • Faster clinical insights
  • Continuous monitoring capability
  • Supports early intervention

Challenges

  • Data variability across patients
  • Missing or noisy clinical data
  • Model generalization
  • Clinical validation requirements