We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables.
- Authors
Kim, Minkyu; Kim, Tae-Hoon; Kim, Dowon; Lee, Donghoon; Kim, Dohyun; Heo, Jeongwon; Kang, Seonguk; Ha, Taejun; Kim, Jinju; Moon, Da Hye; Heo, Yeonjeong; Kim, Woo Jin; Lee, Seung-Joon; Kim, Yoon; Park, Sang Won; Han, Seon-Sook; Choi, Hyun-Soo
- Abstract
Pressure ulcers (PUs) are a prevalent skin disease affecting patients with impaired mobility and in high-risk groups. These ulcers increase patients' suffering, medical expenses, and burden on medical staff. This study introduces a clinical decision support system and verifies it for predicting real-time PU occurrences within the intensive care unit (ICU) by using MIMIC-IV and in-house ICU data. We develop various machine learning (ML) and deep learning (DL) models for predicting PU occurrences in real time using the MIMIC-IV and validate using the MIMIC-IV and Kangwon National University Hospital (KNUH) dataset. To address the challenge of missing values in time series, we propose a novel recurrent neural network model, GRU-D++. This model outperformed other experimental models by achieving the area under the receiver operating characteristic curve (AUROC) of 0.945 for the on-time prediction and AUROC of 0.912 for 48h in-advance prediction. Furthermore, in the external validation with the KNUH dataset, the fine-tuned GRU-D++ model demonstrated superior performances, achieving an AUROC of 0.898 for on-time prediction and an AUROC of 0.897 for 48h in-advance prediction. The proposed GRU-D++, designed to consider temporal information and missing values, stands out for its predictive accuracy. Our findings suggest that this model can significantly alleviate the workload of medical staff and prevent the worsening of patient conditions by enabling timely interventions for PUs in the ICU.
- Subjects
CLINICAL decision support systems; MISSING data (Statistics); PRESSURE ulcers; RECEIVER operating characteristic curves; RECURRENT neural networks
- Publication
Journal of Clinical Medicine, 2024, Vol 13, Issue 1, p36
- ISSN
2077-0383
- Publication type
Article
- DOI
10.3390/jcm13010036