Neural-signature methods for structured EHR prediction
BMC Medical Informatics and Decision Making, ISSN: 1472-6947, Vol: 22, Issue: 1, Page: 320
2022
- 10Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Captures10
- Readers10
- 10
- Mentions1
- News Mentions1
- 1
Most Recent News
University College London (UCL) Reports Findings in Medical Informatics (Neural-signature methods for structured EHR prediction)
2022 DEC 16 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- New research on Health Information Technology - Medical Informatics
Article Description
Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85143492503&origin=inward; http://dx.doi.org/10.1186/s12911-022-02055-6; http://www.ncbi.nlm.nih.gov/pubmed/36476601; https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-022-02055-6; https://dx.doi.org/10.1186/s12911-022-02055-6
Springer Science and Business Media LLC
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