Helpsalud: Endometriosis And Leukaemia Results Report

ITI
2017 · doi:10.5281/zenodo.1133393 · W6930025008
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This project applied machine learning to aid in early breast cancer diagnosis, noninvasive endometriosis detection, and personalized leukemia treatment prediction by integrating diverse data sources.

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The HELPSALUD project applied machine learning and pattern recognition to healthcare challenges, including early diagnosis of breast cancer, noninvasive detection of endometriosis, and prediction of the effects of acute myeloid leukaemia treatment for personalized care. The work involved digitizing the health sector by combining different information sources to develop software intended to support clinical decision-making, aiming for cost savings and improved service outcomes. The document is a results summary after project execution, but it does not provide detailed study design, participant population, or quantitative performance outcomes in the text provided. This paper is centrally about endometriosis — it reports on a HELPSALUD effort for noninvasive detection of endometriosis using machine learning techniques.

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Abstract

HELPSALUD (Research in Machine Learning techniques applied to real problems in the Health sector) is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). The general objective of the project is to advance in the digitization of the health sector through the application of Artificial Intelligence techniques, such as Machine Learning and Pattern Recognition, to the early diagnosis of breast cancer, the noninvasive detection of endometriosis and the prediction of the effects of leukaemia treatment for personalized treatment. With the application of these techniques and the combination of different sources of information, software solutions will be developed to help the clinical staff in the decision-making process, allowing cost savings and improvement of the health service. This document contains a summary of the achieved results after the execution of a project in the line of research of applying machine learning techniques to solve different challenges proposed in the healthcare sector in order to diagnose or prognose illnesses as breast cáncer, endometriosis and acute mieloid leukaemia.
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HELPSALUD: Endometriosis and leukaemia results report Description HELPSALUD (Research in Machine Learning techniques applied to real problems in the Health sector) is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). The general objective of the project is to advance in the digitization of the health sector through the application of Artificial Intelligence techniques, such as Machine Learning and Pattern Recognition, to the early diagnosis of breast cancer, the noninvasive detection of endometriosis and the prediction of the effects of leukaemia treatment for personalized treatment. With the application of these techniques and the combination of different sources of information, software solutions will be developed to help the clinical staff in the decision-making process, allowing cost savings and improvement of the health service. This document contains a summary of the achieved results after the execution of a project in the line of research of applying machine learning techniques to solve different challenges proposed in the healthcare sector in order to diagnose or prognose illnesses as breast cáncer, endometriosis and acute mieloid leukaemia. Notes Files E5_1_Informe de resultados endometriosis y leucemia.pdf Files (1.9 MB) | Name | Size | Download all | |---|---|---| | md5:fa79806812b06930cf113cd35aefd6ce | 1.9 MB | Preview Download |

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