A Predictive Method for Unplanned Postoperative Readmission Risk Based on Heterogeneous Data by Kai YU, Xiaofeng DONG, Zhenming YUAN, Zhaojian CUI, Weibin LUO

“…To address these issues, it is proposed to use machine learning technology combined with patient clinical heterogeneous data to develop a predictive model for unplanned postoperative readmission. …”
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Federated Learning for Predicting Major Postoperative Complications by Yuanfang Ren, PhD, Yonggi Park, PhD, Benjamin Shickel, PhD, Ziyuan Guan, MS, Ayush Patel, Yingbo Ma, PhD, Zhenhong Hu, PhD, Jeremy A. Balch, MD, Tyler J. Loftus, MD, PhD, Parisa Rashidi, PhD, Tezcan Ozrazgat-Baslanti, PhD, Azra Bihorac, MD, MS

“…Conclusions:. We show federated learning to be a useful tool to train robust postoperative outcome prediction models from large-scale data across 2 hospitals.…”
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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach by Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani

“…MethodsThe iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. …”
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Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning by Chunxiu Zhao, Bingbing Xiang, Jie Zhang, Pingliang Yang, Qiaoli Liu, Shun Wang

“…BackgroundPatients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking.ObjectiveTo develop and validate a machine learning model for predicting PPI risk in patients with diabetes.MethodsThis retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023. …”
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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…Moreover, the feature-reduced model showed an AUROC value of 0.785 for predicting the MCID of postoperative JOA in the external dataset, which included 58 patients from other hospitals.ConclusionWe developed models based on machine learning to predict postoperative neurological outcomes. …”
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Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang

“…Abstract Background Clinically relevant postoperative pancreatic fistula (CR-POPF) following laparoscopic pancreaticoduodenectomy (LPD) is a critical complication that significantly worsens patient outcomes. …”
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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Abstract Objective To investigate machine learning‐based regression models to predict the postoperative apnea‐hypopnea index (AHI) for evaluating the outcome of velopharyngeal surgery in adult obstructive sleep apnea (OSA) subjects. …”
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Prediction of postoperative vault after implantable collamer lens implantation with deep learning by Dong-Qing Yuan, Fu-Nan Tang, Ying Wang, Hui Zhang, Wei-Wei Zhang, Liu-Wei Gu, Qing-Huai Liu

“…CONCLUSION: AI effectively predicts postoperative vault and determines ICL size. XGBoost outperforms other machine-learning algorithms tested. …”
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Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery by Humam Baki, İsmail Bülent Özçelik

“…<b>Background/Objectives</b>: Postoperative deep vein thrombosis (DVT) is a common and serious complication after tibial fracture surgery. …”
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Predicting postoperative nausea and vomiting using machine learning: a model development and validation study by Maxim Glebov, Teddy Lazebnik, Maksim Katsin, Boris Orkin, Haim Berkenstadt, Svetlana Bunimovich-Mendrazitsky

“…Abstract Background Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. …”
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Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study by Yaxuan Wang, Shiyang Xie, Jiayun Liu, He Wang, Jiangang Yu, Wenya Li, Aika Guan, Shun Xu, Yong Cui, Wenfei Tan

“…Background Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.Objective We aimed to investigate independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer.Methods This study used data from a previous retrospective study of 16,368 patients with lung cancer (training set: 11,458; validation set: 4,910) with American Standards Association physical statuses I–IV who underwent surgery. …”
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Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients by Zhenmeng Lin, Zhenmeng Lin, Hao He, Mingfang Yan, Xiamei Chen, Hanshen Chen, Jianfang Ke

“…This study aimed to develop and validate predictive models using machine learning algorithms and a nomogram to estimate the risk of malnutrition at 1 month after esophagectomy.MethodsA total of 1,693 patients who underwent curative esophageal cancer surgery were analyzed, with 1,251 patients allocated to the development cohort and 442 to the validation cohort. …”
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Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture by Tang M, Zhang M, Dang Y, Lei M, Zhang D

“…This model can serve as a useful tool to identify postoperative pneumonia and guide clinical strategies for super-aged patients with hip fracture.Keywords: machine learning, postoperative pneumonia, hip fracture, super-aged patients, geriatric patients…”
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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

“…The aim of this study is to predict the POCP status of patients based on perioperative data by developing an “Intelligent POCP Prediction System (I-POCPP)” using the best performing machine learning algorithm. Material and Method: The dataset for this multi-centered study was collected from 5 tertiary hospitals in Turkey and included 733 patients who had undergone elective surgeries attended by an anesthesiologist in the operating room. …”
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Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients by Yu Zhu, Renrui Liang, Ying Wang, Jian-Jun Yang, Ning Zhou, Cheng-Mao Zhou

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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

“…Abstract Background Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in postoperative patients. Therefore, it is particularly important to find a suitable way to predict the risk of postoperative occurrence of DVT in GC patients. …”
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Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis by Feng Pang, Lijiao Wu, Jianping Qiu, Yu Guo, Liangen Xie, Shimin Zhuang, Mengya Du, Danni Liu, Chenyue Tan, Tianrun Liu

“…This study aimed to explore the diagnostic capabilities of computed tomography (CT) imaging and radiomic analysis to distinguish the recurrence of cervical lymph nodes in patients with PTC postoperatively. Materials and methods A retrospective analysis of 194 PTC patients who underwent total thyroidectomy was conducted, with 98 cases of cervical lymph node recurrence and 96 cases without recurrence. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…PurposeThis study aims to develop and validate an interpretable machine learning model for predicting avascular necrosis (AVN) following talar fracture, thereby aiding in personalized prevention and treatment.MethodsA retrospective cohort study included patients undergoing surgical intervention for talar fractures at Ningbo No.6 Hospital between January 2018 and December 2023. …”
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A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumor... by WenJie Xie, Zhen Zhang, Zhao Sun, XiaoChen Wan, JieHan Li, JianWu Jiang, Qi Liu, Ge Yang, Yang Fu

“…We aim to develop individualized clinical follow-up strategies for primary GIST patients, such as shortening follow-up time or extending drug administration based on the clinical deep learning radiomics model (CDLRM). Methods The clinical information on primary GISTs was collected from two independent centers. …”
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