Machine Learning applied to the early detection of diseases.


This R&D project aims to create a Platform that, through an Expert System and ML and Big Data Analysis Algorithms, acts predictively to detect diseases, specifically stroke.

The main idea of this project is that the system will self-learn so that it can launch early warnings. In this way, the Decision Support System (DSS) will alert the clinician when a risk situation is detected so that the appropriate measures can be taken.

Andalusian Health Service

Andalusian Health Service

Created in 1986, is one of the most important Health Services in Europe, servicing more than8 millions of people.

SAS (Andalusian Health Service) has a support of 92.934 professionals(annual average)in order to assist its welfare network: 24.436 for primary medical attention and 68.498 for hospital care.

Diraya is the system used for the Andalusian Public Health System as support for the electronic medical history.

Our solution

Data Collection Techniques
Data Collection Techniques
Information on Standards Techniques associated to medical ontologies such as SNOMED CT and linked analysis of the relationships coming from this connection.
Data mining and KDD application of standard processes applied to the construction of cerebrovascular diseases categorizers according to the HCE.
Machine learning Research
Machine learning Research
Pattern research findings in patients simptomatology and clinic data. Correlation analysis between risk factor and stroke case progress probability,as well as procedure and prescription effectiveness used for treatment. Farm of non-relational database. Semantic algorithm and self-learning detection of stroke probability.
Machine learning Challenge
Machine learning Challenge
Automatic learning scalability to medical pathology detection algorithm,based on theoretic models, tree diagrams and rules that are already verified in other existing medical models. Construction of functional model of pathology learning,such as genetic algorithm, artificial neuronal networks, gathering algorithms or Bayesian networks.

State of the arte enhancements



Creation of an expert system of sanitary software based on Big data, Machine learning and automatic learning Algorithms, capable of discovering hidden symptomatology in electronic records that are not considered or properly evaluated nowadays.

This detection must be used to feed a Decision Support System (DSS) that warns the doctor when it detects a hazardous situation.


Elastic Search
Apache Hbase

The project has been co-financed by the Ministry of Energy, Tourism and Digital Agenda.

Project number: EXP-00095962 / IDI-20161013
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