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Real-world data mining meets clinical practice: Research challenges and perspective.

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Summary
This summary is machine-generated.

This study explores data-driven approaches for healthcare using Artificial Intelligence (AI) and Machine Learning (ML) on Electronic Health Records (EHRs). It addresses challenges in medical data and AI implementation for better clinical decision support and personalized medicine.

Keywords:
P4 medicineartificial intelligenceelectronic health recordshigh-stakes domainsmachine learning

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Area of Science:

  • Healthcare Data Science
  • Clinical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Big Data Analysis is transforming healthcare, enabling the Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm.
  • Electronic Health Records (EHRs) are increasingly utilized for knowledge discovery and clinical decision support through advanced statistical methods and AI.
  • The integration of AI in healthcare presents unique challenges and opportunities across all data science domains.

Purpose of the Study:

  • To describe data-driven (DD) approaches developed for critical clinical applications using real-world EHR data.
  • To share perspectives on challenges encountered in applying AI and Big Data Analytics in a clinical setting.
  • To propose effective techniques for tackling medical data issues and ensuring successful AI implementation in healthcare.

Main Methods:

  • Utilized statistics, Machine Learning (ML), and Big Data Analytics on real-world EHR data.
  • Developed and applied Data-Driven (DD) approaches within the Infectious Disease Clinic of the University Hospital of Modena, Italy.
  • Focused on addressing challenges related to the sparse, scarce, and unbalanced nature of medical data.

Main Results:

  • Identified key challenges in medical data characteristics (sparse, scarce, unbalanced) and the clinical application environment.
  • Presented available techniques to overcome these data and implementation challenges.
  • Provided examples from practical experience to illustrate proposed solutions.

Conclusions:

  • Successful real-world, end-to-end implementations of AI in healthcare require addressing specific data and environmental challenges.
  • Data-driven approaches, when carefully applied, can significantly enhance clinical decision support and advance personalized medicine.
  • Further research and development are needed to optimize AI tools for safe and effective use in patient care.