Real-world data mining meets clinical practice: Research challenges and perspective. Mandreoli, F., Ferrari, D., Guidetti, V., Motta, F., & Missier, P. Frontiers in Big Data, 2022. ![Real-world data mining meets clinical practice: Research challenges and perspective [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@ARTICLE{10.3389/fdata.2022.1021621,
AUTHOR={Mandreoli, Federica and Ferrari, Davide and Guidetti, Veronica and Motta, Federico and Missier, Paolo},
TITLE={Real-world data mining meets clinical practice: Research challenges and perspective},
JOURNAL={Frontiers in Big Data},
VOLUME={5},
YEAR={2022},
URL={https://www.frontiersin.org/articles/10.3389/fdata.2022.1021621},
DOI={10.3389/fdata.2022.1021621},
ISSN={2624-909X},
ABSTRACT={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.}
}
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