An Optimized Pipeline for the Processing of Healthcare Data towards the Creation of Holistic Health Records
Our research paper entitled “An Optimized Pipeline for the Processing of Healthcare Data towards the Creation of Holistic Health Records” has been
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Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records
SOURCE: Article in Journal – Sensors; MDPI, Published: 7 March 2024 Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine
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Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records
Our research paper entitled “Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records”
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An Optimized Pipeline for the Processing of Healthcare Data towards the Creation of Holistic Health Records
SOURCE: 2023 International Conference on Applied Mathematics & Computer Science (ICAMCS); Publisher: IEEE, Published: 20 February 2024 An Optimized Pipeline for the
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iHELP Engaging Participation in the Upcoming Webinar on Health Sector Standardization
In an era defined by groundbreaking technological advancements, the pursuit of standardized practices within the healthcare sector is an imperative goal. Enter
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Building the iHelp Platform
The modern healthcare landscape and market As the healthcare domain continues to become more complex with a tremendous amount of data being
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iHelp partner introduction – HDM
Dénia Health Department belongs to the Marina Alta area, a region of the Valencian Community in Spain. This health department is managed
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The potential for Artificial Intelligence in Healthcare Data
The iHelp project will focus on the early identification and mitigation of the risks associated with Pancreatic Cancer. More specifically, it aims
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Data collection and advice delivery in iHelp using Healthentia
The iHelp project aims at early identification and mitigation of the risks associated with cancer (with a particular focus on pancreatic cancer), based on the application of advanced Machine Learning techniques on
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