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Li W, Zhang Y, Zhou X, Quan X, Chen B, Hou X, Xu Q, He W, Chen L, Liu X, Zhang Y, Xiang T, Li R, Liu Q, Wu SN, Wang K, Liu W, Zheng J, Luan H, Yu X, Chen A, Xu C, Luo T, Hu Z. Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study. J Orthop Surg Res 2024; 19:112. [PMID: 38308336 PMCID: PMC10838003 DOI: 10.1186/s13018-024-04576-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/23/2024] [Indexed: 02/04/2024] Open
Abstract
PURPOSE This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS gov/ct2/show/NCT05867732 .
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Affiliation(s)
- Wenle Li
- State Key Laboratory of Molecular Vaccinology and Molecular, Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
- Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
- Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China.
| | - Yusi Zhang
- Cancer Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xin Zhou
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China
| | - Xubin Quan
- Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China
| | - Binghao Chen
- Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China
| | - Xuewen Hou
- Department of Radiology, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China
| | - Qizhong Xu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Weiheng He
- Department of Radiology, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
| | - Liang Chen
- Department of Radiology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Xiaozhu Liu
- Department of Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
| | - Tianyu Xiang
- Information Center, The University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Runmin Li
- Department of Foot and Ankle Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Qiang Liu
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, Shannxi, China
| | - Shi-Nan Wu
- Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Kai Wang
- Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Wencai Liu
- Department of Orthopedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Jialiang Zheng
- Cancer Research Center, School of Medicine, Xiamen University, Xiamen, China
| | - Haopeng Luan
- Department of Spine Surgery, The Six Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaolin Yu
- Department of Orthopedics, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Anfa Chen
- Department of Orthopedics, Jiangxi Province Hospital of Integrated Chinese and Western Medicine, Nanchang, China
| | - Chan Xu
- State Key Laboratory of Molecular Vaccinology and Molecular, Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China
| | - Tongqing Luo
- Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China.
| | - Zhaohui Hu
- Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China.
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Yan W, Tan L, Meng-Shan L, Sheng S, Jun W, Fu-an W. SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction. PeerJ 2023; 11:e16192. [PMID: 37810796 PMCID: PMC10559882 DOI: 10.7717/peerj.16192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/06/2023] [Indexed: 10/10/2023] Open
Abstract
Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.
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Affiliation(s)
- Wu Yan
- School of Biotechnology, Jiangsu University of Science & Technology, Zhenjiang, China
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, Jiangxi, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, China
| | - Li Tan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
| | - Li Meng-Shan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, China
| | - Sheng Sheng
- School of Biotechnology, Jiangsu University of Science & Technology, Zhenjiang, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, China
| | - Wang Jun
- School of Biotechnology, Jiangsu University of Science & Technology, Zhenjiang, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, China
| | - Wu Fu-an
- School of Biotechnology, Jiangsu University of Science & Technology, Zhenjiang, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, China
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