Semi‑supervised incremental learning with few examples for discovering medical association rules

Sánchez‑de‑Madariaga, Ricardo, Martinez‑Romo, Juan, Cantero Escribano, José Miguel y Araujo, Lourdes . (2022) Semi‑supervised incremental learning with few examples for discovering medical association rules. BMC Medical Informatics and Decision Making

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Título Semi‑supervised incremental learning with few examples for discovering medical association rules
Autor(es) Sánchez‑de‑Madariaga, Ricardo
Martinez‑Romo, Juan
Cantero Escribano, José Miguel
Araujo, Lourdes
Materia(s) Biomedicina
Ingeniería Informática
Abstract Background: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time‑consuming. The purpose of this research is to design a new semi‑supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. Methods: In this work we propose a new semi‑supervised data mining model that combines unsupervised techniques (Fisher’s exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F‑measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. Results: The new semi‑supervised ML algorithm improves the results of supervised algorithms computed using the F‑measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. Conclusions: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.
Palabras clave Medical records
Association rules discovery
Machine learning
Semi‑supervised approach
Editor(es) BioMed Central
Fecha 2022
Formato application/pdf
Identificador bibliuned:DptoLSI-ETSI-Articulos-Laraujo-0002
http://e-spacio.uned.es/fez/view/bibliuned:DptoLSI-ETSI-Articulos-Laraujo-0002
DOI - identifier https://doi.org/10.1186/s12911‑022‑01755‑3
ISSN - identifier 1472-6947
Nombre de la revista BMC Medical Informatics and Decision Making
Número de Volumen 22
Número de Issue 1
Página inicial 2
Página final 11
Publicado en la Revista BMC Medical Informatics and Decision Making
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en BMC Medical Informatics and Decision Making (2022) 1-2 p.2-11, está disponible en línea en el sitio web del editor: BioMed Central, https://doi.org/10.1186/s12911‑022‑01755‑3
Notas adicionales The registered version of this article, first published in BMC Medical Informatics and Decision Making (2022) 1-2 p.2-11, is available online at the publisher's website: BioMed Central, https://doi.org/10.1186/s12911-022-01755-3

 
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Creado: Wed, 20 Mar 2024, 21:59:51 CET