Machine Learning

Intraoperative Hypotension Predictor

Evidence-based ML model that predicts IOH risk using 14 clinical features. Trained on 10,000 synthetic cases based on published research.

Educational Tool Only: This ML model is designed for learning about IOH risk prediction. It is NOT intended for clinical decision-making or actual patient care. Always consult evidence-based guidelines and clinical judgment for real patient management.

Patient & Hemodynamic Parameters

Enter current intraoperative data to estimate IOH risk using our RandomForest classifier.

Patient Information

Age*

Sex*

Weight (kg)*

Height (cm)*

ASA Class*

Hemodynamic Parameters

Baseline MAP (mmHg)*

Baseline HR (bpm)*

Current MAP (mmHg)*

MAP 5 min ago*

MAP 10 min ago*

Surgical & Anesthetic Factors

Surgery Type*

Surgery Duration (min)*

Induction Agent*

Vasopressor in Use*

Emergency Surgery*

How the ML Model Works

Our RandomForest classifier provides transparent, evidence-based IOH risk predictions. Here's how it works:

Training Dataset

Trained on 10,000 synthetic cases generated from published clinical research on IOH risk factors. Training data reflects real-world IOH prevalence and risk patterns from literature.

14 Clinical Features

Model analyzes demographics (age, sex, BMI, ASA), hemodynamics (MAP trends, HR), and surgical factors (type, duration, induction agent, emergency status).

RandomForest Algorithm

Uses 200 decision trees with balanced class weights and feature scaling. Test accuracy: ~85%. Most important features: current MAP, MAP trends, age, and ASA class.

Evidence-Based

Risk factors weighted based on published literature: Sessler 2015 (MAP thresholds), Hatib 2018 (ML prediction), Walsh 2013 (organ injury), Reich 2005 (anesthetic effects).

Interpretable Outputs

Provides 3 time-windowed predictions (5/10/20 min) with decay modeling. Identifies top risk factors and suggests evidence-based interventions for clinical context.

Continuous Improvement

Model can be retrained and updated as new clinical evidence emerges. Currently optimized for general surgical cases. Not validated on real patient data.