An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations.
Comput Biol Med
; 168: 107754, 2024 01.
Article
em En
| MEDLINE
| ID: mdl-38016372
ABSTRACT
Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure injury prediction algorithms have been developed to tackle this problem, but these models assume a consensus, gold-standard label (i.e., presence of pressure injury or not) is present for all training data. Existing definitions for identifying hospital-acquired pressure injuries are inconsistent due to the lack of high-quality documentation surrounding pressure injuries. To address this issue, we propose in this paper an ensemble-based algorithm that leverages truth inference methods to resolve label inconsistencies between various case definitions and the level of disagreements in annotations. Application of our method to MIMIC-III, a publicly available intensive care unit dataset, gives empirical results that illustrate the promise of learning a prediction model using truth inference-based labels and observed conflict among annotators.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Úlcera por Pressão
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article