XTRACTIS FOR PREDICTIVE MEDECINE

Spectrometric diagnosis of ovarian cancer

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to make an automated —yet transparent— medical diagnosis of ovarian cancer, from a sample of serum analyzed by a mass spectrometer?

Goals & benefits

Identify the proteins involved in cancer, from the spectrum bands.

Enhance medical knowledge by helping gynecologists and oncologists understand the causal relationships between specific proteins, their combination, and the presence of cancer.

Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses.

Contribute to improving patient care (pain, survival, duration of treatment) and extend access to high-level diagnoses even in medical deserts.

XTRACTIS RESULTS

Predictive Model that is:

Intelligible.

A Decision System composed of 2 unchained gradual rules using only the 3 variables that xtractis identified as significant.

Robust.

Perfect 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 09/2022 (v1.3)

Results by
XTRACTIS® GENERATE 12.1.42004 (09/2022)

DOCUMENT CONTENTS

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