Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx
Background<p>Enteral Nutrition-Associated Diarrhea (ENAD) is a common complication in critically ill patients, significantly impacting clinical outcomes. Accurately predicting the risk of ENAD is crucial for early intervention and improving patient care.</p>Objective<p>This study a...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852019069907107840 |
|---|---|
| author | Xiaoying Liao (21159071) |
| author2 | Chunhua Li (18618) Qunyan Liu (21596501) Wang Xia (1565947) Zhenglin Liu (19261930) Jiamao Zhu (21596504) Wei Hu (6560) Qionghua Hong (9911361) |
| author2_role | author author author author author author author |
| author_facet | Xiaoying Liao (21159071) Chunhua Li (18618) Qunyan Liu (21596501) Wang Xia (1565947) Zhenglin Liu (19261930) Jiamao Zhu (21596504) Wei Hu (6560) Qionghua Hong (9911361) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xiaoying Liao (21159071) Chunhua Li (18618) Qunyan Liu (21596501) Wang Xia (1565947) Zhenglin Liu (19261930) Jiamao Zhu (21596504) Wei Hu (6560) Qionghua Hong (9911361) |
| dc.date.none.fl_str_mv | 2025-06-25T04:07:11Z |
| dc.identifier.none.fl_str_mv | 10.3389/fnut.2025.1584717.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table_2_Machine_learning-based_predictive_model_for_enteral_nutrition-associated_diarrhea_in_ICU_patients_and_its_nursing_applications_docx/29397359 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Clinical and Sports Nutrition enteral nutrition-associated diarrhea machine learning random forest feature importance critically ill patients |
| dc.title.none.fl_str_mv | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Enteral Nutrition-Associated Diarrhea (ENAD) is a common complication in critically ill patients, significantly impacting clinical outcomes. Accurately predicting the risk of ENAD is crucial for early intervention and improving patient care.</p>Objective<p>This study aims to develop and validate a machine learning (ML)-based risk prediction model for Enteral Nutrition-Associated Diarrhea (ENAD) in ICU patients, and explore its application in nursing practice.</p>Method<p>This study was conducted from January 2023 to October 2024 in the Comprehensive Intensive Care Unit (ICU) of a tertiary hospital in China, retrospectively analyzing data from ICU patients receiving enteral nutrition. LASSO regression was used for feature selection, and 9 machine learning (ML) algorithms were evaluated. Model performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanation (SHAP) method was employed to interpret feature importance and determine the final model.</p>Results<p>Among the 9 ML models, the random forest (RF) model demonstrated the highest discriminative ability, achieving an AUC (95% CI) of 0.777 (0.702–0.830). After dimensionality reduction based on feature importance analysis, a simplified and interpretable RF model with 12 key predictors was established, yielding an AUC (95% CI) of 0.754 (0.685–0.823).</p>Conclusion<p>The RF-based predictive model developed in this study provides a reliable and interpretable tool for identifying the risk of ENAD in ICU patients, contributing to targeted nursing interventions and improved patient outcomes. The research highlights the potential of machine learning in enhancing clinical decision-making and personalized care.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_feedbbeef15dd9ebfdc331abc6583e40 |
| identifier_str_mv | 10.3389/fnut.2025.1584717.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29397359 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docxXiaoying Liao (21159071)Chunhua Li (18618)Qunyan Liu (21596501)Wang Xia (1565947)Zhenglin Liu (19261930)Jiamao Zhu (21596504)Wei Hu (6560)Qionghua Hong (9911361)Clinical and Sports Nutritionenteral nutrition-associated diarrheamachine learningrandom forestfeature importancecritically ill patientsBackground<p>Enteral Nutrition-Associated Diarrhea (ENAD) is a common complication in critically ill patients, significantly impacting clinical outcomes. Accurately predicting the risk of ENAD is crucial for early intervention and improving patient care.</p>Objective<p>This study aims to develop and validate a machine learning (ML)-based risk prediction model for Enteral Nutrition-Associated Diarrhea (ENAD) in ICU patients, and explore its application in nursing practice.</p>Method<p>This study was conducted from January 2023 to October 2024 in the Comprehensive Intensive Care Unit (ICU) of a tertiary hospital in China, retrospectively analyzing data from ICU patients receiving enteral nutrition. LASSO regression was used for feature selection, and 9 machine learning (ML) algorithms were evaluated. Model performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanation (SHAP) method was employed to interpret feature importance and determine the final model.</p>Results<p>Among the 9 ML models, the random forest (RF) model demonstrated the highest discriminative ability, achieving an AUC (95% CI) of 0.777 (0.702–0.830). After dimensionality reduction based on feature importance analysis, a simplified and interpretable RF model with 12 key predictors was established, yielding an AUC (95% CI) of 0.754 (0.685–0.823).</p>Conclusion<p>The RF-based predictive model developed in this study provides a reliable and interpretable tool for identifying the risk of ENAD in ICU patients, contributing to targeted nursing interventions and improved patient outcomes. The research highlights the potential of machine learning in enhancing clinical decision-making and personalized care.</p>2025-06-25T04:07:11ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fnut.2025.1584717.s002https://figshare.com/articles/dataset/Table_2_Machine_learning-based_predictive_model_for_enteral_nutrition-associated_diarrhea_in_ICU_patients_and_its_nursing_applications_docx/29397359CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293973592025-06-25T04:07:11Z |
| spellingShingle | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx Xiaoying Liao (21159071) Clinical and Sports Nutrition enteral nutrition-associated diarrhea machine learning random forest feature importance critically ill patients |
| status_str | publishedVersion |
| title | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| title_full | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| title_fullStr | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| title_full_unstemmed | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| title_short | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| title_sort | Table 2_Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications.docx |
| topic | Clinical and Sports Nutrition enteral nutrition-associated diarrhea machine learning random forest feature importance critically ill patients |