Public Use Cases in Health & Pharma
Our Use Cases present the results of XTRACTIS modeling and include complete Benchmarks against Neural Networks, Boosted Trees, Random Forests, and Logistic Regression.
These studies illustrate the ability of XTRACTIS to automatically induce knowledge in the form of predictive and intelligible mathematical relationships from real-world data (public data or authorized private data).
For each application, we show how the induced decision system uses its fuzzy rules to compute explained predictions for new situations, i.e. unknown to the learning data set.
The benchmarks of XTRACTIS, Logistic Regression, Random Forest, Boosted Tree, and Neural Network are carried out on Test and External Test datasets (all the unknown cases of the reference dataset, i.e., not used for training or validation).
In this graph, the Intelligibility Score IS and the Performance Score PS are calculated from the public Use Cases (UC) in the Health and Pharma sectors and that include benchmarks of models.
The center of a bubble corresponds to the point of coordinates IS -on top- and PS -on bottom. A bubble on the top-right corner is the Holy Grail for critical AI-based decision systems: an AI Technique which produces predictive models with the highest Performance and the highest Intelligibility.
All quantitative metrics are defined in the use cases documents.
XTRACTIS discovers how to detect a diabetic condition oppotunistically from the patient’s blood test and demographic characteristics. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to detect the epileptic seizure from the patient’s electroencephalogram (EEG) processed signal. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to identify foetus heart conditions based on signal characteristics of fetal heart rate and the mother’s uterine contractions. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to detect breast cancer based on topological characteristics of mammary cells. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to detect ovarian cancer based on the 15,154 mass-charge ratios coming from the protein spectrum of serum samples, and for a small number of patients. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to detect prostate cancer based on the expression levels of 12,600 genes and for a small number of patients. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to identify 2 types of lung cancer based on the expression levels of 12,533 genes and for a small number of patients. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to detect kidney disease based on the patient record and blood measures. The result is an explainable and accurate automated medical diagnosis.
XTRACTIS discovers how to detect Parkinson’s disease based on simple voice recordings of patients. The result is an explainable and accurate automated medical diagnosis.
