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Other literature type . 2025
License: CC BY
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Presentation . 2025
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2025
License: CC BY
Data sources: Datacite
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An overview of causal machine learning methodology in Electronic Health Records

Authors: O'Connell, Maurice; Sperrin, Matthew;

An overview of causal machine learning methodology in Electronic Health Records

Abstract

Dr Maurice O’Connell gave a talk on current work with Dr Matthew Sperrin and the DynAIRx team in the area of statistics and causal inference and causal machine learning. Maurice talked about causal inference methodology development applied to electronic health records in the areas of deprescribing or continuing\initiating medications in individuals and populations with polypharmacy and multimorbidity and selecting individuals with polypharmacy and multimorbidity for structured medication reviews. This session was organised by the AI for Multiple Long Term Conditions Research Support Facility (link to the same archived website). AIM RSF is funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme (NIHR202647).

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green