Technical Feasibility of Implementing and Commercializing a Machine Learning Model for Rare Disease Prediction-Reference-Cited by-同舟云学术

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Technical Feasibility of Implementing and Commercializing a Machine Learning Model for Rare Disease Prediction

Published:2023 Issue: Volume:11 Page:84430-84439
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Koutitas George¹^ORCID,Nolen Kimberly²^ORCID,Attal Sepideh³,Ventouris Anastasios¹^ORCID,Dolev Yinnon⁴^ORCID,Van Den Broek Hans Thijs⁵^ORCID

Affiliation:

1. Pfizer Center for Digital Innovation, Thessaloniki, Greece

2. Medical Affairs, Pfizer, New York, NY, USA

3. Medical Affairs, Paris, Pfizer, France

4. Diagnostic and Pathway Informatics, Haifa, Philips, Israel

5. Diagnostic and Pathway Informatics, Philips Healthcare, Eindhoven, AE, The Netherlands

Funder

Pfizer

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10196444.pdf?arnumber=10196444

Reference32 articles.

1. Imaging Intelligence: AI Is Transforming Medical Imaging Across the Imaging Spectrum

2. A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

3. Healthcare in the Age of Interoperability: The Promise of Fast Healthcare Interoperability Resources

4. HL7 FHIR: An Agile and RESTful approach to healthcare information exchange

5. Association of an Electronic Health Record Add-on App for Neonatal Bilirubin Management With Physician Efficiency and Care Quality

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1. Heart Disease Prediction using Random Forest Classifier;2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI);2023-12-21

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