An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations.

Autor: Ho JC; Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, 30322, GA, USA. Electronic address: joyce.c.ho@emory.edu., Sotoodeh M; Canadian Institute for Health Information, 495 Richmond Road, Suite 600 - WS-602, Ottawa, K2A 4H6, Ontario, Canada., Zhang W; Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, 30322, GA, USA., Simpson RL; Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, 30322, GA, USA., Hertzberg VS; Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, 30322, GA, USA.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Jan; Vol. 168, pp. 107754. Date of Electronic Publication: 2023 Nov 22.
DOI: 10.1016/j.compbiomed.2023.107754
Abstrakt: 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.
Competing Interests: Declaration of competing interest None declared.
(Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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