دورية أكاديمية
أعرض في EDS

Causal machine learning for healthcare and precision medicine.

التفاصيل البيبلوغرافية
العنوان: Causal machine learning for healthcare and precision medicine.
المؤلفون: Sanchez P; School of Engineering, University of Edinburgh, Edinburgh, UK., Voisey JP; AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK., Xia T; School of Engineering, University of Edinburgh, Edinburgh, UK., Watson HI; AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK., O'Neil AQ; School of Engineering, University of Edinburgh, Edinburgh, UK.; AI Research, Canon Medical Research Europe, Edinburgh, Lothian, UK., Tsaftaris SA; School of Engineering, University of Edinburgh, Edinburgh, UK.
المصدر: Royal Society open science [R Soc Open Sci] 2022 Aug 03; Vol. 9 (8), pp. 220638. Date of Electronic Publication: 2022 Aug 03 (Print Publication: 2022).
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Royal Society Publishing Country of Publication: England NLM ID: 101647528 Publication Model: eCollection Cited Medium: Print ISSN: 2054-5703 (Print) Linking ISSN: 20545703 NLM ISO Abbreviation: R Soc Open Sci Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: London : Royal Society Publishing, 2014-
مستخلص: 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.
(© 2022 The Authors.)
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فهرسة مساهمة: Keywords: causal machine learning; causal representation learning; precision medicine
تواريخ الأحداث: Date Created: 20220811 Latest Revision: 20230425
رمز التحديث: 20230425
مُعرف محوري في PubMed: PMC9346354
DOI: 10.1098/rsos.220638
PMID: 35950198
قاعدة البيانات: MEDLINE
الوصف
تدمد:2054-5703
DOI:10.1098/rsos.220638