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Cited by 13Pressure Ulcer Prevention and ManagementMachine Learning in HealthcareArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index, and 1 are not checked yet.

In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables

Open access
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·2023·Journal of Clinical Medicine
DOI 10.3390/jcm13010036PMC PMC10780209
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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.

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Crossref · 10.3390/jcm13010036

Cites

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Figures

Figure A1 An example of X,X˜,M,M˜,s,andΔ. This time series has three variables and six time steps. N/A = not applicable.
Figure A1 An example of X,X˜,M,M˜,s,andΔ. This time series has three variables and six time steps. N/A = not applicable.CC BY · PubMed Central
Figure 1 List of input variables.
Figure 1 List of input variables.CC BY · PubMed Central
Figure 2 Flow chart of the preprocessing steps.
Figure 2 Flow chart of the preprocessing steps.CC BY · PubMed Central
Figure 3 Overview of the proposed in-advance PU prediction system. The blue cell indicates the patient’s admission time, and the red cell indicates the PU occurrence time. Here, xt represents the input variables updated every hour, and x′t represents imputed complete variables. Furthermore, yt is the output of whether PU occurs or not, which was used for training and evaluating our model. The definition of yt can be changed according to the in-advance prediction setting. For example, in 24 h of in-advance prediction, if PU occurs 60 h after admission, the first “yes” should be y36.
Figure 3 Overview of the proposed in-advance PU prediction system. The blue cell indicates the patient’s admission time, and the red cell indicates the PU occurrence time. Here, xt represents the input variables updated every hour, and x′t represents imputed complete variables. Furthermore, yt is the output of whether PU occurs or not, which was used for training and evaluating our model. The definition of yt can be changed according to the in-advance prediction setting. For example, in 24 h of in-advance prediction, if PU occurs 60 h after admission, the first “yes” should be y36.CC BY · PubMed Central
Figure 4 SHAP values of top ten influential variables.
Figure 4 SHAP values of top ten influential variables.CC BY · PubMed Central

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Cites

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Research works printed but not linked: 1

  1. PAN Pacific (2014). Prevention and treatment of pressure ulcers: Quick reference guide. Natl. Press. Ulcer Advis. Panel, 75, 1–72.

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Metadata

Authors
Kim, M., Kim, T.-H., Kim, D., Lee, D., Kim, D., Heo, J., Kang, S., Ha, T., Kim, J., Moon, D. H., Heo, Y., Kim, W. J., Lee, S.-J., Kim, Y., Park, S. W., Han, S.-S., & Choi, H.-S.
Affiliations
  • Department of Research & Development, Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
  • Kangwon National University (KR)
  • Kangwon National University Hospital (KR)
  • Seoul National University of Science and Technology (KR)
Corresponding author
  • Han, Seon-Sook
  • Choi, Hyun-Soo
ORCID
Venue
Journal of Clinical Medicine
Publisher
MDPI AG
Published
2023-12-20
Volume
13
Issue
1
Pages
36
ISSN
2077-0383 (ISSN-L)
DOI
10.3390/jcm13010036
PMC
PMC10780209
OpenAlex
W4390014060
Cited by
13
References
25
Citations by year
2026: 7 · 2025: 5 · 2024: 1
FWCI
6.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Clinical Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Korea Health Industry Development Institute (KHIDI) (HI21C1074)
Topics
  • Health Sciences › Medicine › Dermatology › Pressure Ulcer Prevention and Management
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
pressure ulcers, pressure ulcer prediction, intensive care unit, missing data imputation, recurrent neural networks, clinical decision support systems, MIMIC-IV, intensive care monitoring

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