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
AMIA Annual Symposium proceedings AMIA Symposium
; 2022:442-451, 2022.
Article
in English
| EuropePMC | ID: covidwho-2295013
ABSTRACT
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.
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Collection:
Databases of international organizations
Database:
EuropePMC
Type of study:
Prognostic study
Language:
English
Journal:
AMIA Annual Symposium proceedings AMIA Symposium
Year:
2022
Document Type:
Article
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