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Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quanti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9346354/ https://www.ncbi.nlm.nih.gov/pubmed/35950198 http://dx.doi.org/10.1098/rsos.220638 |
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author | Sanchez, Pedro Voisey, Jeremy P. Xia, Tian Watson, Hannah I. O’Neil, Alison Q. Tsaftaris, Sotirios A. |
author_facet | Sanchez, Pedro Voisey, Jeremy P. Xia, Tian Watson, Hannah I. O’Neil, Alison Q. Tsaftaris, Sotirios A. |
author_sort | Sanchez, Pedro |
collection | PubMed |
description | Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made while maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer’s disease to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges. |
format | Online Article Text |
id | pubmed-9346354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-93463542022-08-09 Causal machine learning for healthcare and precision medicine Sanchez, Pedro Voisey, Jeremy P. Xia, Tian Watson, Hannah I. O’Neil, Alison Q. Tsaftaris, Sotirios A. R Soc Open Sci Computer Science and Artificial Intelligence Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made while maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer’s disease to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges. The Royal Society 2022-08-03 /pmc/articles/PMC9346354/ /pubmed/35950198 http://dx.doi.org/10.1098/rsos.220638 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Computer Science and Artificial Intelligence Sanchez, Pedro Voisey, Jeremy P. Xia, Tian Watson, Hannah I. O’Neil, Alison Q. Tsaftaris, Sotirios A. Causal machine learning for healthcare and precision medicine |
title | Causal machine learning for healthcare and precision medicine |
title_full | Causal machine learning for healthcare and precision medicine |
title_fullStr | Causal machine learning for healthcare and precision medicine |
title_full_unstemmed | Causal machine learning for healthcare and precision medicine |
title_short | Causal machine learning for healthcare and precision medicine |
title_sort | causal machine learning for healthcare and precision medicine |
topic | Computer Science and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9346354/ https://www.ncbi.nlm.nih.gov/pubmed/35950198 http://dx.doi.org/10.1098/rsos.220638 |
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