Development of a Machine Learning-Based Nutrition-Related Surgical Risk Assessment Model for Older Patients with Gastrointestinal Malignancies.
Older patients with gastrointestinal cancer are at a high risk of postoperative complications; however, no accurate preoperative assessment is available. This study developed a prognostic model that leveraged machine learning and multidimensional clinical data to predict postoperative complications...
| Publicado en: | Nutrition & Cancer Vol. 77; no. 10; pp. 1121 - 1132 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188316559&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188316559 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01635581 7MS jtl: Nutrition & Cancer issn: 01635581 maglogo: N pubinfo: dt: 2025 vid: 77 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 188316559 187689375 188316559 188316559 10.1080/01635581.2025.2552461 188316559 ppf: 1121 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of a Machine Learning-Based Nutrition-Related Surgical Risk Assessment Model for Older Patients with Gastrointestinal Malignancies. aug: au: Yin, Shishu Liu, Xu Cao, Xianglong Cui, Jian Shi, Jinxin Ma, Fuhai Ma, Tianming An, Qi Yu, Tao Li, Zijian Zhao, Gang affil: Department of General Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P.R. China sug: subj: Machine Learning Prediction Models Risk Assessment Nutritional Assessment Malnutrition Risk Factors Postoperative Complications Risk Factors Stomach Neoplasms Surgery Colorectal Neoplasms Surgery Funding Source China Human Retrospective Design Random Assignment ROC Curve Data Analysis Software Body Mass Index Hemoglobins Albumins Comorbidity Patient Safety Quality Improvement Hospitalization of Older Persons Scales Activities of Daily Living Questionnaires Prediction Algorithms Male Female Aged Aged, 80 and Over Random Forest Boosting Machine Learning Algorithms Support Vector Machine Logistic Regression Descriptive Statistics Aged: 65+ years Aged, 80 & over Male Female ab: Older patients with gastrointestinal cancer are at a high risk of postoperative complications; however, no accurate preoperative assessment is available. This study developed a prognostic model that leveraged machine learning and multidimensional clinical data to predict postoperative complications in older patients. This study assessed 365 older patients with gastrointestinal cancer who underwent radical surgery at Beijing Hospital. Patients were randomly allocated to training and test sets (7:3 ratio). Multiplex machine learning was used for feature selection and model development. The efficacies of the models were assessed using receiver operating characteristic curves. An imbalance rfsrc + ranger model (IRM) was created using the "shiny" R package. All statistical analyses were performed using R software. The overall rate of postoperative complications was 19.2%. IRM was the most accurate among the 361 models developed using 19 machine learning algorithms and 19 sets of clinical features. Body mass index was the most important variable for predicting postoperative complications in these patients, followed by hemoglobin level, albumin level, and surgical approach. This study developed a nutrition-related surgical risk assessment model that includes malnutrition, comorbidities, and surgical approaches to improve the outcome of older patients with gastrointestinal malignancies, aiding in managing preoperative risk factors and improving surgical safety. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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