Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, 30322, GA, USA.
Canadian Institute for Health Information, 495 Richmond Road, Suite 600 - WS-602, Ottawa, K2A 4H6, Ontario, Canada.
Comput Biol Med. 2024 Jan;168:107754. doi: 10.1016/j.compbiomed.2023.107754. Epub 2023 Nov 22.
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.
医院获得性压疮是临床环境中最具危害性的事件之一。如果患者得不到早期预防和治疗，可能会面临巨大的经济负担和身体创伤。为了解决这个问题，已经开发出了几种医院获得性压疮预测算法，但这些模型假设所有训练数据都存在共识的、黄金标准的标签（即是否存在压疮）。由于缺乏高质量的压疮相关文档，现有的医院获得性压疮识别定义并不一致。针对这个问题，我们在本文中提出了一种基于集成的算法，该算法利用真实推理方法来解决各种病例定义之间的标签不一致问题，以及注释者之间的分歧程度。我们的方法应用于公开的重症监护病房数据集 MIMIC-III，实证结果表明，使用真实推理标签和观察到的注释者之间的冲突来学习预测模型是有前景的。
