Abstract
Hospitalization of patients is one of the major factors for high wound care costs. Most patients do not acquire a wound which needs immediate hospitalization. However, due to factors such as delay in treatment, patient's non-compliance or existing co-morbid conditions, an injury can deteriorate and ultimately lead to patient hospitalization. In this paper, we propose a deep multi-modal method to predict the patient's risk of hospitalization. Our goal is to predict the risk confidently by collectively using the wound variables and wound images of the patient. Existing works in this domain have mainly focused on healing trajectories based on distinct wound types. We developed a transfer learning-based wound assessment solution, which can predict both wound variables from wound images and their healing trajectories, which is our primary contribution. We argue that the development of a novel model can help in early detection of the complexities in the wound, which might affect the healing process and also reduce the time spent by a clinician to diagnose the wound.
Abstract (translated)
住院是导致伤口护理成本高昂的主要因素之一。大多数患者受伤后不需要立即住院治疗，但因延误治疗、患者的不依从性或现有合并症等因素，伤情可能会恶化并最终导致病人入院。本文提出了一种深度多模态方法来预测病人的住院风险。我们的目标是通过综合使用伤口变量和伤口图像信息，准确地预测住院的风险。 在该领域现有的研究大多集中在基于不同类型伤口的愈合轨迹上。我们开发了一种基于迁移学习的伤口评估解决方案，能够从伤口图像中预测伤口变量及其愈合轨迹，这是本文的主要贡献。我们认为，新型模型的发展可以帮助及早发现可能影响愈合过程的伤口复杂性，并减少临床医生诊断伤口所需的时间。
URL
https://arxiv.org/abs/2602.09315
