[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125105-en":3,"doc-seo-125105-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125105,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Enhancing clinical decision-making in closed pelvic fractures with machine learning models","Closed pelvic fractures can cause hemodynamic instability and death, making accurate risk prediction critical for timely intervention. This retrospective study analyzed 208 pelvic fracture patients admitted between 2019 and 2023, training on clinically collected variables to distinguish hemodynamic outcomes. Seven machine learning models and factor analysis were used to predict hemodynamic instability and mortality risk, including logistic regression and random forest. Results showed random forest and logistic regression outperformed traditional central venous pressure and intra-abdominal pressure measures, with risk factors including TILE grade, heart rate, creatinine, blood cell counts, fibrinogen, and lactic acid.","RESEARCH ARTICLE  \nEnhancing clinical decision-making in closed pelvic fractures with machine learning models  \nDian Wang 1 ∗, Yongxin Li 2, and Li Wang 2  \nClosed pelvic fractures (PFs) can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML) algorithms to predict HI and death in patients with closed PFs and identify relevant risk factors. The retrospective study included 208 patients diagnosed with PFs and admitted to Suning Traditional Chinese Medicine Hospital between 2019 and 2023. Among these, 133 cases were identified as closed PFs. Patients with closed fractures were divided into a training set (n = 115) and a test set (n = 18) . The training set was further stratified into two groups based on hemodynamic stability: Group A (patients with HI) and Group B (patients with hemodynamic stability). A total of 40 clinical variables were collected, and multiple ML algorithms were employed to develop predictive models, including logistic regression (LR), C5.0 decision tree, Naive Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and artificial neural network (ANN). Additionally, factor analysis was performed to assess the interrelationships between variables. The RF and LR algorithms outperformed traditional methods—such as central venous pressure (CVP) and intra-abdominal pressure (IAP) measurements—in predicting HI. The RF model achieved an average area under the ROC curve (AUC) of 0.92, with an accuracy of 0.86, precision of 0.81, and an F1 score of 0.87. The LR model had an average AUC of 0.82 but shared the same accuracy, precision, and F1 score as the RF model. Key risk factors identified included TILE grade, heart rate (HR), creatinine (CR), white blood cell (WBC) count, fibrinogen (FIB), and lactic acid (LAC), with LAC levels >3.7 and an Injury Severity Score (ISS) >13 as significant predictors of HI and mortality. In conclusion, the RF and LR algorithms are effective in predicting HI and mortality risk in patients with closed PFs, enhancing clinical decision-making and improving patient outcomes.  \nKeywords: Hemodynamic instability, HI, closed pelvic fracture, PF, machine learning, ML, risk prediction, clinical decision-making, mortality risk.  \nIntroduction  \nPelvic fractures (PFs), a common type of traumatic injury, posea serious threat to patient safety [1] . Among these, closed PFs are particularly concerning due to the rich vascularity surrounding the pelvic bones, which increases the risk of severe internal bleeding and hemodynamic instability (HI) following a fracture [2–4] . Such cases often require urgent medical intervention to prevent fatal outcomes. However, the complexity and variability of closed PFs make it challenging for clinicians to accurately assess associated risks [5] . Current assessment methods primarily rely on intuitive clinical judgment and traditional monitoring of physiological parameters. While helpful for diagnosis, these methods have significant limitations in predicting HI and mortality risks. Traditionally, physicians evaluate the hemodynamic status of PF patients using central venous pressure (CVP) and intra-abdominal pressure (IAP) measurements [6–8]. Despite their widespread use, these methods are limited in their ability to predict long-term patient  \noutcomes [9–11] . For example, CVP and IAP readings can beinfluenced by numerous factors, failing to reliably reflect the severity of HI [12] . Moreover, these approaches provide little direct insight into patients’ mortality risks [13–15] . As a result, there is an urgent need for advanced predictive tools that offer more accurate assessments, enabling clinicians to better understand patient conditions and make informed decisions. In recent years, machine learning (ML) technologies have gained significant attention in the medical fiel","cbCait789M1cqD2c","https://ap.wps.com/l/cbCait789M1cqD2c","pdf",4146065,1,17,"English","en",105,"# Introduction\n## Background and clinical challenge\n## Limitations of traditional monitoring\n## Role of machine learning in prognosis","[{\"question\":\"What risks are targeted for prediction in closed pelvic fractures?\",\"answer\":\"The study predicts hemodynamic instability and mortality risk following closed pelvic fractures.\"},{\"question\":\"Which machine learning models were used to build the predictive systems?\",\"answer\":\"Models included logistic regression, C5.0 decision tree, Naive Bayes, support vector machine, K-nearest neighbors, random forest, and artificial neural network.\"},{\"question\":\"Which model performed best compared with traditional CVP and IAP measurements?\",\"answer\":\"Random forest and logistic regression outperformed traditional central venous pressure and intra-abdominal pressure approaches in predicting hemodynamic instability.\"}]","Enhancing clinical decision-making in closed pelvic fractures with machine learning models | 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risks are targeted for prediction in closed pelvic fractures?","Question",{"text":75,"@type":76},"The study predicts hemodynamic instability and mortality risk following closed pelvic fractures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used to build the predictive systems?",{"text":80,"@type":76},"Models included logistic regression, C5.0 decision tree, Naive Bayes, support vector machine, K-nearest neighbors, random forest, and artificial neural network.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best compared with traditional CVP and IAP measurements?",{"text":84,"@type":76},"Random forest and logistic regression outperformed traditional central venous pressure and intra-abdominal pressure approaches in predicting hemodynamic 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