XTRACTIS FOR PREDICTIVE MEDECINE

Serological Diagnosis of Chronic Kidney Disease

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

How can an automated — yet totally transparent — medical diagnosis of chronic kidney disease be made from the patient’s record and blood measures?

Goals & benefits

Identify the parameters involved in the kidney disease and enhance medical knowledge by helping nephrologists understand the causal relationships between these parameters, their combination, and the disease.

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

Use a model with few predictors to limit medical data that can be expensive to collect.

XTRACTIS RESULTS

We get a Predictive Model that is:

Intelligible.

A Decision System composed of 4 unchained gradual rules, each using 8 variables that XTRACTIS identified as significant (out of 24 potential predictors characterizing each patient).

Robust.

Perfect Real Performance on External Test.

Efficient & Operational.

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

full use case

 Use Case 2023/02 (v1.1)

Results by
XTRACTIS® GENERATE 13.0.44983 (2023/02)

DOCUMENT CONTENTS

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