{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["35(3)"],"submitter":["Li P"],"pubmed_abstract":["Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688-0.768) with the sensitivity "],"journal":["Aging clinical and experimental research"],"pagination":["639-647"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10014765"],"repository":["biostudies-literature"],"pubmed_title":["Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study."],"pmcid":["PMC10014765"],"pubmed_authors":["Hu S","Hu T","Fang D","Hu X","Zhan X","Lin X","Su L","Wang XL","Xue R","Hu CH","Yuan Y","Xu L","He C","Zeng Q","He J","Feng Y","Xu Q","He M","He L","Yuan J","Yuan D","Yuan F","He X","Lin H","Hu N","Kong CC","Xu S","Lin J","Hu L","Hu R","International Surgical Outcomes Study (ISOS) group in China","Lei M","Meng S","Zhu Z","Zhu Y","Yu S","Cao J","Zhao W","Zhao T","Lwang L","Zhao X","Fan H","Zhao L","Zhao S","Qi X","Fan L","Qi Y","Yu T","Li B","Wei J","Zhao J","Yu Y","Zhou XJ","Zhao C","Fan Y","Zhao B","Bai Y","Meng B","Cao Z","Cao Y","Zhu X","Ye H","Du X","Xiong X","Xiong Y","Qiu X","Xia J","Chen Z","Chen Y","Ou Y","Shan Q","Chen S","Chen R","Ai Y","Dong B","Xia Y","Li H","Chen Q","Li J","Chen P","Li C","Chen J","Chen L","Li P","Chen F","Li Q","Zhong Y","Chen H","Chen C","Li L","Li M","Liu CX","Li X","Li Y","Li Z","Pei L","Jiang L","Ma L","Liu M","Jia D","Liu FF","Liu Q","Cai F","Liu W","Zou X","Zheng K","Liu Y","Jiang Z","Liu Z","Zheng L","Jiang X","Bai HP","Meng QT","Zhuang X","Luo F","Luo H","Luo D","Zhang W","Hou J","Zhang Y","Zhang X","Cai Y","Hou B","Liu B","Yin N","Ma D","Luo S","Cang J","Bi Y","Liu G","Zhang E","Shen J","Zhang K","Guo Z","Guo Y","Zhang J","Shi P","Liang Y","Zhang H","Zhang M","Shen C","Zhang L","Wan X","Zhang S","Zhang R","Shi X","Zhang Q","Shi Y","Liang D","Liang G","Guo H","Guo F","Guo B","Zhang B","Shen ZY","Liang Q","Jin L","Jin S","Fei Y","Zheng T","Ren W","Fu S","Geng W","Han K","Lv X","Qin J","Ren Y","Fu Y","Han Y","Cheng Z","Tang J","Hwan H","Tang N","Zhou C","Zhou D","Zhou H","Tang Y","Zhou J","Mo Y","Ren Q","Ling Y","Cheng B","Xiao W","Tong Y","Lv J","Huang S","Lv M","Huang Y","Peng ZD","Dai Q","Huang W","Fu K","Qian L","Dai H","Qian M","Qin C","Wen Y","Huang C","Wen Z","Lu X","Huang J","Wu Q","Sun C","Wang D","Huang H","Wu C","Ouyang L","Wang F","Wang G","Gao H","Wang H","Huang L","Wang J","Wang K","Wang L","Lian J","Xie G","Wang S","Xie K","Xie J","Wang T","Ge X","Wu S","Wang W","Wang Y","Sun H","Zhen J","Wang Z","Yang J","Yang L","Yao S","Sun Y","Lu B","Lu K","Min J","Yang Y","Leng Y","Yang Q","Yang T","Yang S","Jing L","Miao F","Xu GJ","Jing Y","Yao Y","Mu H","Jie W","Song X","Gong H","Song Z","Tao J","Ji B","Pan Y","Tao F","Wen XH","Yan Y","Bao Q","Fang X","Xia ZY","Qu PS","Ji Z","Miao Z"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study.","description":"Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688-0.768) with the sensitivity ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2025-04-22T08:51:27.652Z","creation":"2024-12-03T14:46:35.326Z"},"accession":"S-EPMC10014765","cross_references":{"pubmed":["36598653"],"doi":["10.1007/s40520-022-02325-3"]}}