[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121412-en":3,"doc-seo-121412-105":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":20,"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},121412,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Prediction of Post-Surgical Complications in Hand Reconstruction Using Machine Learning","Hand reconstruction is a complex upper-limb procedure with diverse postoperative complications, including infections, flap necrosis, and joint stiffness. Traditional prediction relies on surgeon experience and conventional clinical models, which can be limited by subjective interpretation and inefficient integration of multiple risk factors. A retrospective cohort of 200 patients was analyzed using clinical record data to train and validate machine learning models, aiming to improve predictive accuracy and support clinical decision-making.","DOI: [https://dx.doi.org/10.18203/2320-6012.ijrms20251625](https://dx.doi.org/10.18203/2320-6012.ijrms20251625)  \nOriginal Research Article  \nPrediction of post-surgical complications in hand reconstruction  \nusing machine learning  \nIrvin Hernandez Sanchez*, Mauricio Gerardo Martínez Morales, Daniela Maria Mercado Botero  \nDepartment of General Surgery, National Autonomous University of Mexico (UNAM), General Hospital of Mexico, Mexico City, Mexico  \nReceived: 09 April 2025  \nRevised: 10 May 2025  \nAccepted: 20 May 2025  \n*Correspondence:  \nDr. Irvin Hernandez Sanchez,  \nE-mail: [irvg25@hotmail.com](irvg25@hotmail.com)  \nCopyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nABSTRACT  \nBackground: Hand reconstruction is a complex surgical procedure in which various postoperative complications mayarise, such as infections, flap necrosis, and joint stiffness. The prediction of these complications has traditionally relied on the surgeon’s experience and conventional clinical models. However, artificial intelligence (AI), particularly machine learning, has proven to be an effective tool for analyzing large volumes of clinical data and enhancing predictive capabilities in various medical fields.  \nMethods: A retrospective study was conducted with a sample of 200 patients who underwent hand reconstruction, using exclusively clinical record data. Three machine learning models were evaluated: XGBoost, Random Forest, and an artificial neural network. A data preprocessing pipeline, feature selection, and cross-validation were applied to optimize model performance. Predictive capability was assessed using the ROC curve and the area under the curve (AUC) . Results: XGBoost achieved the best performance with an AUC of 0.88, followed by Random Forest (AUC = 0.88) and the artificial neural network (AUC = 0.86) . The most relevant variables for predicting complications included patient age, comorbidities such as diabetes mellitus, type of injury, and surgery duration.  \nConclusions: AI models proved to be useful tools for predicting postoperative complications in hand reconstruction, surpassing the accuracy of conventional methods. In particular, XGBoost demonstrated the highest predictive capacity. These findings suggest that machine learning could optimize surgical planning and clinical decision-making, although further studies are needed to validate its applicability across different populations.  \nKeywords: Artificial intelligence, Hand reconstruction, Machine learning, Postoperative complications  \nINTRODUCTION  \nHand reconstruction surgeries are highly specialized procedures within plastic and reconstructive surgery, with the primary goal of restoring both function and aesthetics of the upper limb. These interventions may be required due to various etiologies, including severe trauma, burns, infections, congenital diseases, and sequelae of chronic conditions such as rheumatoid arthritis or diabetes  \nmellitus.¹ Hand reconstruction requires a multidisciplinary approach involving microsurgery, tissue flaps, grafts, and osteosynthesis techniques to preserve patient mobility and sensitivity.² Despite advances in surgical techniques and postoperative care, postoperative complications remain a significant challenge. The most common complications include surgical wound infection, flap necrosis, joint stiffness, fibrosis, complex regional pain syndrome, and wound dehiscence³ . These complications can compromise the functional outcomes of surgery, affect the patient’s  \nquality of life, prolong recovery times, and increase treatment costs.⁴  \nCurrently, the prediction of these complications relies mainly on the surgeon’s clinical experience and traditional risk assessment models","cbCaiqTcsaNcXOK3","https://ap.wps.com/l/cbCaiqTcsaNcXOK3","pdf",203157,1,5,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Study design and data\n## Machine learning models\n## Model evaluation metrics\n# Results\n# Conclusions","[{\"question\":\"What postoperative complications are considered in predicting outcomes after hand reconstruction?\",\"answer\":\"The study focuses on complications such as surgical wound infection, flap necrosis, and joint stiffness, reflecting key clinical challenges after reconstruction.\"},{\"question\":\"Which machine learning models were evaluated for complication prediction?\",\"answer\":\"Three models were assessed: XGBoost, Random Forest, and an artificial neural network, with performance compared using ROC and AUC.\"},{\"question\":\"What variables were most relevant for predicting postoperative complications?\",\"answer\":\"Patient age, comorbidities like diabetes mellitus, injury type, and surgery duration were identified as the most relevant predictive variables.\"}]","Prediction of Post-Surgical Complications in Hand Reconstruction Using Machine Learning | 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postoperative complications are considered in predicting outcomes after hand reconstruction?","Question",{"text":75,"@type":76},"The study focuses on complications such as surgical wound infection, flap necrosis, and joint stiffness, reflecting key clinical challenges after reconstruction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were evaluated for complication prediction?",{"text":80,"@type":76},"Three models were assessed: XGBoost, Random Forest, and an artificial neural network, with performance compared using ROC and AUC.",{"name":82,"@type":73,"acceptedAnswer":83},"What variables were most relevant for predicting postoperative complications?",{"text":84,"@type":76},"Patient age, comorbidities like diabetes mellitus, injury type, and surgery duration were identified as the most relevant predictive 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