XTRACTIS FOR PREDICTIVE MEDECINE

CARDIOTOCOGRAPHIC IDENTIFICATION OF FETAL HEART PATHOLOGIES

Benchmark vs. Random Forests, Boosted Trees & Neural Networks

How to make an automated —yet transparent— medical diagnosis of fetal heart disease from signal characteristics of fetal heart rate and uterine contractions?

Goals & benefits

Identify parameters requiring increased vigilance and improve medical knowledge by helping cardiologists understand the causal relationships between specific cardiotocographic features, their combination, and the presence of an abnormality.

Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses. Extend access to high-level diagnoses even in medical deserts

Decrease prenatal mortality and avoid possible neurological sequelae for the fetus.

XTRACTIS RESULTS

Predictive Model that is:

Intelligible.

A Decision System composed of 56 unchained gradual rules, each using some of the 18 variables that XTRACTIS identified as significant including weak signals.

Robust.

Good performance on External Test.

Efficient & Operational.

Running in real-time up to 70,000 predictions per second (i7, 8 physical cores, 2.5GHz), offline or online (API).

Summary of Use Case 05/2022 (v1.2)

Results by
XTRACTIS® GENERATE 12.1.41978 (05/2022)

DOCUMENT CONTENTS

  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Predictions for 4 cases
  5. Top-Models Benchmark