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Untangling the complexity of multimorbidity with machine learning.

Abdelaali Hassaine1, Gholamreza Salimi-Khorshidi2, Dexter Canoy1

  • 1Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.

Mechanisms of Ageing and Development
|August 10, 2020
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Machine learning offers powerful tools to understand complex multimorbidity patterns. These advanced methods help identify disease links and improve healthcare delivery by analyzing evolving health trends.

Keywords:
Deep learningElectronic health recordsMachine learningMultimorbidityPhenotyping

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Area of Science:

  • Computational biology
  • Health informatics
  • Data science in medicine

Background:

  • Increasing prevalence of multimorbidity presents significant healthcare challenges.
  • Traditional research methods struggle to capture complex disease interactions.
  • Multimorbidity research requires advanced analytical approaches beyond single-disease studies.

Purpose of the Study:

  • To review machine learning tools for addressing multimorbidity research challenges.
  • To highlight advanced machine learning methods applicable to multimorbidity.
  • To discuss opportunities and challenges of machine learning in understanding disease associations.

Main Methods:

  • Review of machine learning techniques including matrix factorization, deep learning, and topological data analysis.
  • Exploration of how these methods can analyze evolving patterns of multimorbidity.
  • Discussion on using machine learning to identify causal links and estimate uncertainty in disease associations.

Main Results:

  • Machine learning enables a move beyond cross-sectional and expert-driven approaches in multimorbidity research.
  • Advanced methods offer potential for understanding complex, evolving patterns of multiple chronic conditions.
  • Machine learning can identify potential causal links between diseases with quantifiable uncertainty.

Conclusions:

  • Machine learning provides novel opportunities to advance multimorbidity research.
  • These tools can offer deeper insights into disease co-occurrence and progression.
  • Addressing challenges in clinical adoption is crucial for realizing the full potential of these research tools.