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BIGSALUD

Big Data and Artificial Intelligence for healthcare system optimization

Field
Regional
Date
01/01/2022 - 30/06/2023
Industry
  • Salud
Budget
240557,71
Funded by

IVACE

PROJECT INFORMATION

DESCRIPTION

The main objective of this project is to deepen the research of Big Data and Artificial Intelligence techniques, mainly Machine Learning and Deep Learning, for the improvement of diagnosis and prognosis techniques for chronic diseases and cancer. At the same time, these technologies will make it possible to optimize processes to reduce time and healthcare costs, thus contributing to the sustainability of healthcare systems in Europe. The project combines data sets from various sources to build predictive models as a clinical and hospital decision support system. The data sources used as a basis are, among others: medical history, genomic information, medical images, pharmacy, lifestyle habits, etc.
The project is aimed at optimizing disease management through research in software techniques based on Machine Learning in order to assist clinical staff in the decision-making process, enabling better diagnosis and prognosis of diseases and a more personalized and effective treatment of patients.

The main objective of this project is to deepen the research of Big Data and Artificial Intelligence techniques, mainly Machine Learning and Deep Learning, for the improvement of diagnosis and prognosis techniques for chronic diseases and cancer. At the same time, these technologies will make it possible to optimize processes to reduce time and healthcare costs, thus contributing to the sustainability of healthcare systems in Europe. The project combines data sets from various sources to build predictive models as a clinical and hospital decision support system. The data sources used as a basis are, among others: medical history, genomic information, medical images, pharmacy, lifestyle habits, etc.
The project is aimed at optimizing disease management through research in software techniques based on Machine Learning in order to assist clinical staff in the decision-making process, enabling better diagnosis and prognosis of diseases and a more personalized and effective treatment of patients.

Impact

The objectives of the project are articulated in the context of the applicability of Machine Learning techniques to the health sector, which have proven their effectiveness in other fields such as biometrics, handwritten text recognition, machine translation, etc. On the other hand, it is about framing it in a Big Data Analytics environment that, by nature, allows both robust storage and massive computations, all in an agile, elastic and scalable way. By innovatively combining infrastructure services for information processing, both at the storage and distributed processing levels, with Artificial Intelligence, we hope to provide novel solutions to problems related to precision medicine, such as those mentioned above. The aim is to improve the quality of life with a medicine where treatments are tailored to each patient and where a hospital can better anticipate their needs.

The objectives are focused on the following axes:

- Methodology for adapting health data. Define a common workflow between medical teams and data analysts to exchange, understand and transform health data (medical images, genomic information, clinical information, etc.) in a suitable way for its analysis with Machine Learning techniques.
- Health data analysis through Machine Learning techniques using Big Data tools.
Develop and apply Machine Learning techniques in a Big Data environment to produce predictive models from health data in order to issue diagnoses or forecasts that help specialists in their decision making.
- Diagnostic and Prognostic Tool for the diagnosis and prognosis of the disease
Design and develop a multi-platform web service where clinical staff can enter patient data into a system based on Artificial Intelligence, to obtain a prediction (diagnosis or prognosis) in real time.

Technological capabilities

IA
Predictive and prescriptive analytical technologies