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ISSN: 2641-1725

LOJ Medical Sciences

Research Article(ISSN: 2641-1725)

A Perspective: Use of Machine Learning Models to Predict the Risk of Multimorbidity Volume 5 - Issue 5

Gayathri Delanerolle1*, Suchith Shetty4, Vanessa Raymont6, Dharani Hapangama10, Nyla Haque6, Jian Shi8,9, Salma Ayis7, Ashish Shetty2,3 and Peter Phiri4,5

  • 1Nuffield Department of Primary Health Care Sciences, University of Oxford, UK
  • 2University College London Hospitals NHS Foundation Trust, UK
  • 3University College London, UK
  • 4Southern Health NHS Foundation Trust, UK
  • 5Pyschology Department, Faculty of Environmental and Life Sciences, University of Southampton, UK
  • 6Department of Psychiatry, University of Oxford, UK
  • 7Population Health Science, Kings College London, UK
  • 8Southern University of Science and Technology, UK
  • 9The Alan Turin Institute, UK
  • 10University of Liverpool, UK

Received: July 27, 2021   Published: September 14, 2021

*Corresponding author: Gayathri Delanerolle, Mail:

DOI: 10.32474/LOJMS.2021.05.000225

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Machine Learning (ML) is a common Artificial Intelligence (AI) method. The use of ML offers the opportunity to develop better data mining techniques in order to analyse complex clinical interactions with a large number of variables. ML models should provide “real-time” clinical support reducing clinical risk to patients with model-agnostic interpretation to deduce a more specific clinical decision. Whilst ML algorithms have been used as the relatively “new kid on the block” in healthcare practice, they have shown promising results in predicting disease outcomes or risks in a variety of diseases such as depressive disorder, Type 2 diabetes mellitus, postoperative complications and cardiovascular diseases. However, patients suffering from a chronic condition are likely to have more than one condition requiring simultaneous attention and care. Therefore, a risk assessment model developed using ML methods, in theory, would be suitable to evaluate multimorbid populations. While there are many AI/ML algorithms and methods to build such a risk assessment tool, an optimal ‘fit-for-purpose’ model is chosen by comparing and contrasting across many possible alternatives. Furthermore, given the high-stake decisions associated with health, it is also important that the model is interpretable and explainable by the clinicians who are purported to use such a model as their decision support system. In this paper, we provide a perspective on the current landscape of multimorbidity treatment, potential benefit of employing AI/ML to enhance holistic care of multimorbid patients, and associated challenges, concerns that need to be addressed as we make progress in this direction.

Abstract| Importance of Multimorbidity| Multimorbidity Diagnoses and Treatment| Machine Learning and Precision Medicine| Machine Learning and Risk Prediction| ML Interpretability| Machine Learning Disease Risk Prediction and Ethics| Conclusion| Acknowledgements| References|


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