Browse by author
Lookup NU author(s): Professor Paolo MissierORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2022 Mandreoli, Ferrari, Guidetti, Motta and Missier.As Big Data Analysis meets healthcare applications, domain-specific challenges and opportunities materialize in all aspects of data science. Advanced statistical methods and Artificial Intelligence (AI) on Electronic Health Records (EHRs) are used both for knowledge discovery purposes and clinical decision support. Such techniques enable the emerging Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm. Working with the Infectious Disease Clinic of the University Hospital of Modena, Italy, we have developed a range of Data–Driven (DD) approaches to solve critical clinical applications using statistics, Machine Learning (ML) and Big Data Analytics on real-world EHR. Here, we describe our perspective on the challenges we encountered. Some are connected to medical data and their sparse, scarce, and unbalanced nature. Others are bound to the application environment, as medical AI tools can affect people's health and life. For each of these problems, we report some available techniques to tackle them, present examples drawn from our experience, and propose which approaches, in our opinion, could lead to successful real-world, end-to-end implementations. DESY report number: DESY-22-153.
Author(s): Mandreoli F, Ferrari D, Guidetti V, Motta F, Missier P
Publication type: Article
Publication status: Published
Journal: Frontiers in Big Data
Year: 2022
Volume: 5
Online publication date: 21/10/2022
Acceptance date: 28/09/2022
Date deposited: 21/11/2022
ISSN (electronic): 2624-909X
Publisher: Frontiers Media S.A.
URL: https://doi.org/10.3389/fdata.2022.1021621
DOI: 10.3389/fdata.2022.1021621
Altmetrics provided by Altmetric
