Untangling the complexity of multimorbidity with machine learning
The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In th...
| Main Authors: | , , , |
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| פורמט: | Journal article |
| שפה: | English |
| יצא לאור: |
Elsevier
2020
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| _version_ | 1826291306993811456 |
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| author | Hassaine, A Salimi-Khorshidi, G Canoy, D Rahimi, K |
| author_facet | Hassaine, A Salimi-Khorshidi, G Canoy, D Rahimi, K |
| author_sort | Hassaine, A |
| collection | OXFORD |
| description | The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions. |
| first_indexed | 2024-03-07T02:57:28Z |
| format | Journal article |
| id | oxford-uuid:afd09d8c-dbe5-4c06-b40a-9d5411e34c9a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:57:28Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:afd09d8c-dbe5-4c06-b40a-9d5411e34c9a2022-03-27T03:52:00ZUntangling the complexity of multimorbidity with machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:afd09d8c-dbe5-4c06-b40a-9d5411e34c9aEnglishSymplectic ElementsElsevier2020Hassaine, ASalimi-Khorshidi, GCanoy, DRahimi, KThe prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions. |
| spellingShingle | Hassaine, A Salimi-Khorshidi, G Canoy, D Rahimi, K Untangling the complexity of multimorbidity with machine learning |
| title | Untangling the complexity of multimorbidity with machine learning |
| title_full | Untangling the complexity of multimorbidity with machine learning |
| title_fullStr | Untangling the complexity of multimorbidity with machine learning |
| title_full_unstemmed | Untangling the complexity of multimorbidity with machine learning |
| title_short | Untangling the complexity of multimorbidity with machine learning |
| title_sort | untangling the complexity of multimorbidity with machine learning |
| work_keys_str_mv | AT hassainea untanglingthecomplexityofmultimorbiditywithmachinelearning AT salimikhorshidig untanglingthecomplexityofmultimorbiditywithmachinelearning AT canoyd untanglingthecomplexityofmultimorbiditywithmachinelearning AT rahimik untanglingthecomplexityofmultimorbiditywithmachinelearning |