Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery

Christina Messiou, Richard Lee, Manuel Salto-Tellez

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
65 Downloads (Pure)

Abstract

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): . This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.
Original languageEnglish
Pages (from-to)4536-4539
Number of pages4
JournalComputational and Structural Biotechnology Journal
Volume21
DOIs
Publication statusPublished - 22 Sept 2023

Keywords

  • Integrated Diagnostics and Discovery
  • Multimodal Analysis
  • Radiomics
  • Digital Pathology
  • Computational Oncology
  • Artificial Intelligence
  • Health data

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