Multi-objective Symbolic Regression to Generate Data-driven, Non-fixed Structure and Intelligible Mortality Predictors using EHR: Binary Classification Methodology and Comparison with State-of-the-art.


Journal

AMIA ... Annual Symposium proceedings. AMIA Symposium
ISSN: 1942-597X
Titre abrégé: AMIA Annu Symp Proc
Pays: United States
ID NLM: 101209213

Informations de publication

Date de publication:
2022
Historique:
medline: 3 5 2023
pubmed: 2 5 2023
entrez: 2 5 2023
Statut: epublish

Résumé

Symbolic Regression (SR) is a data-driven methodology based on Genetic Programming, and it is widely used to produce arithmetic expressions for modelling learning tasks. Compared to other popular statistical techniques, SR outcomes are given by an arbitrary set of mathematical operations, representing arbitrarily complex linear and non-linear functions without a predefined fixed structure. Another advantage is that, unlike other machine learning algorithms, SR produces interpretable results. In this paper, we explore the qualities and limitations of this technique in a novel implementation as a binary classifier for in-hospital or short-term mortality prediction in patients with Covid-19. Our results highlight that SR provides a competitive alternative to popular statistical and machine learning methodologies to model relevant clinical phenomena thanks to good classification performance, stability in unbalanced dataset management, and intrinsic interpretability.

Identifiants

pubmed: 37128446
pii: 511
pmc: PMC10148348

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

442-451

Subventions

Organisme : Department of Health
ID : RJ112/N027
Pays : United Kingdom

Informations de copyright

©2022 AMIA - All rights reserved.

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Auteurs

Davide Ferrari (D)

School of Population Health and Environmental Sciences, King's College London, London, UK.
Centre for Clinical Infection & Diagnostics Research, St. Thomas' Hospital, London, UK.
NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK.

Veronica Guidetti (V)

Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany.

Yanzhong Wang (Y)

School of Population Health and Environmental Sciences, King's College London, London, UK.
NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK.

Vasa Curcin (V)

School of Population Health and Environmental Sciences, King's College London, London, UK.
NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK.

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Classifications MeSH