检索结果-内蒙古大学图书馆

Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2025年第1期25卷 1-13页

作者： Zhang, Haofang Xu, Changbao Hu, Chenge Xue, Yunlai Yao, Daoke Hu, Yifan Wu, Ankang Dai, Miao Ye, Hang Zhengzhou Univ Affiliated Hosp 2 Dept Urol Zhengzhou 450000 Peoples R China Zhengzhou Univ Clin Med Sch 2 2 Jingba Rd Zhengzhou 450000 Peoples R China

BackgroundThe flexible ureteroscopy lithotripsy (F-URL) is an important treatment for upper urinary tract stones. However, urolithiasis, surgical procedures, and catheter placement are risk factors for fungal infections. Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following *** study retrospectively collected the clinical data of patients who underwent F-URL at the Second Affiliated Hospital of Zhengzhou University from January 2016 to March 2024. The patients were divided into a non-fungal infection group and a fungal infection group based on whether a fungal infection occurred within three months post-surgery. The patient data from January 2016 to December 2023 were used as training data, and the patient data from January 2024 to March 2024 were used as testing set. The training data was randomly divided into a training set and validation set at a ratio of 90:10. Use LASSO regression to screen clinical features based on the training set. Nine machine learning algorithms, Logistic Regression (LR), k-Nearest Neighbours (KNN), Support Vector machines (SVM), Random Forest (RF), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting machines (GBM), and Neural Network (NNet), were used to construct models. The performance of these nine models was evaluated and the best predictive model was selected based on the validation set, and evaluate the best predictive model's generalization ability using the testing set. Visualize the constructed optimal machine learning model using the SHapley additive interpretation (SHAP) value method. SHAP force plots were established to show the application of the prediction model at the individual *** total of 13 clinical features were used to construct predictive models: age, diabetes mellitus (DM), history of malignancy, being bedridden, admission white blood cells (WBC), preoperat

关键词： Flexible ureteroscopy lithotripsy machine learning algorithms Fungal infection Risk factors XGBoost model

来源：评论

学校读者我要写书评

暂无评论

Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters

引用

BMC ANESTHESIOLOGY 2024年第1期24卷 1-14页

作者： Sezari, Parisa Kohzadi, Zeinab Dabbagh, Ali Jafari, Alireza Khoshtinatan, Saba Mottaghi, Kamran Kohzadi, Zahra Rahmatizadeh, Shahabedin Shahid Beheshti Univ Med Sci Sch Med Dept Anesthesiol Tehran Iran Shahid Beheshti Univ Med Sci Sch Allied Med Sci Dept Hlth Informat Technol & Management 1th floor 21 Darband St Tajrish sq Tehran Iran Shahid Beheshti Univ Med Sci Anesthesiol Res Ctr Sch Med Dept Anesthesiol Tehran Iran Ilam Univ Med Sci Ilam Cty Hlth Ctr Ilam Iran

BackgroundTo protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway *** study was cross-sectional. The Shahid Beheshti University of Medical Sciences in Iran's Loghman Hakim and Shahid Labbafinezhad hospitals provided 622 records in total for analysis. Using the forest of trees approach and feature importance, important features were chosen. The Synthetic Minority Oversampling Technique (SMOTE) and repeated edited nearest neighbor under-sampling were used to balance the data. Using Python and 10-fold cross-validation, seven machine learning algorithms were assessed: Logistic Regression, Support Vector machines (SVM), Random Forest (INFORMATION-GAIN and GINI-INDEX), Decision Tree, and K-Nearest Neighbors (KNN). Metrics like F-measure, AUC, Recall, Accuracy, Specificity, and Precision were used to evaluate the performance of the ***-four important features were chosen from the original 32 features. The under-sampling strategy produced better results than SMOTE. Among the algorithms, KNN (Euclidean, Minkowski) had better performance than other algorithms. The highest values for accuracy, precision, recall, F-measure, and AUC were obtained at 0.87, 0.88, 0.82, 0.85, and 0.87, *** for machine learning provide insightful information for anticipating challenging airway management. By making it possible to forecast airway difficulties more accurately, these techniques can potentially improve clinical practice and patient outcomes.

关键词： Difficult airway Airway management Intratracheal intubation Anesthesia machine learning algorithms Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Efficient estimation of plant species diversity in desert regions using UAV-based quadrats and advanced machine learning techniques

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 385卷 125614页

作者： Xin, Huihui Zhang, Renping Zhang, Liangliang Xu, Haoen Yu, Xiaoyu Gou, Xueping Gao, Zhengjie Xinjiang Univ Coll Ecol & Environm Urumqi 830046 Peoples R China Xinjiang Univ Educ Minist Key Lab Oasis Ecol Urumqi 830046 Peoples R China Xinjiang Uygur Autonomous Reg Grassland Gen Stn Urumqi 830000 Peoples R China Chinese Acad Sci Inst Bot State Key Lab Vegetat & Environm Change Beijing 100093 Peoples R China

Understanding the distribution of plant species diversity(PSD) along spatial and environmental gradients is essential for implementing effective conservation strategies. However, effective monitoring of large-scale PSD in desert regions remain challenging. In this study, traditional and unmanned aerial vehicle (UAV) quadrat surveys were employed to monitor the vegetation composition in the desert regions of the Junggar Basin, China. By combining multi-source data, two variable selection methods (elastic net regression and Boruta) and two machine learning algorithms (support vector machines and boosted regression trees) were used to develop PSD estimation models. This study aimed to investigate spatiotemporal variations in PSD and their driving factors. The results are as follows. (1) UAV method is more efficient and accurate than traditional methods in investigating PSD in desert areas. (2) The model combining variables selected by Elastic Net Regression and the Boosted Regression Trees algorithm is the optimal model for estimating PSD in desert areas(R2 = 0.476-0.613, RMSE = 0.135-2.2, MAE = 0.1-1.72). (3) The central region of the basin exhibited lower PSD, whereas the peripheral regions demonstrated higher PSD but were more heavily impacted by external disturbances. Over the past 20 years, 5.99 %-13.87 % of the area has shown a significant decline in PSD. (4) Cumulative precipitation and soil organic carbon are the primary drivers of PSD's spatial patterns, while human disturbance dictates its temporal dynamics. This study introduced a novel method for estimating PSD, providing a theoretical foundation for ecological restoration, and biodiversity conservation in the study region.

关键词： Species diversity UAV monitoring machine learning algorithms Spatiotemporal patterns Driving factors

来源：评论

学校读者我要写书评

暂无评论

Runoff Modeling in Ungauged Catchments Using machine learning Algorithm-Based Model Parameters Regionalization Methodology

引用

Engineering 2023年第9期28卷 93-104页

作者： Houfa Wu Jianyun Zhang Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang State Key Laboratory of Hydraulics and Mountain River Engineering College of Water Resource and HydropowerSichuan UniversityChengdu 610065China State Key Laboratory of HydrologyWater Resources and Hydraulic Engineering Nanjing Hydraulic Research InstituteNanjing 210029China Research Center for Climate Change Ministry of Water ResourcesNanjing 210029China

Model parameters estimation is a pivotal issue for runoff modeling in ungauged *** nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used *** modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the *** descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological *** quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in ***,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor ***,the similaritybased regionalization scheme was used for comparison with the regression-based *** results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged *** with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear *** performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid *** the study area contained nested catchments,the best result was calculated with the similarity-based

关键词： Parameters estimation Ungauged catchments Regionalization scheme machine learning algorithms Soil and water assessment tool model

来源：评论

学校读者我要写书评

暂无评论

Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 1-33页

作者： Porto, Bruno Matos Fogliatto, Flavio Sanson Univ Fed Rio Grande do Sul Ind Engn Dept Av Osvaldo Aranha995th Floor BR-90020035 Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Ind Engn Dept Av Osvaldo Aranha995th Floor BR-90035190 Porto Alegre Brazil

BackgroundEmergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three *** machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting machine (LightGBM), Support Vector machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. algorithms' hyperparameters were tuned through a grid-search with cross-validation. Prediction performance was assessed using fivefold cross-validation and four performance *** eXtreme Gradient Boosting (XGBoost) was the best-performing model on both prediction horizons, also outperforming results reported in past studies on ED arrival prediction. XGBoost and NNAR achieved the best performance in nine out of the eleven analyzed datasets, with MAPE values ranging from 5.03% to 14.1%. Feature engineering (FE) improved the performance of the ML *** in predicting ED arrivals, achieved through the FE approach, is key for managing human and material resources, as well as reducing patient waiting times and lengths of stay.

关键词： Emergency department Patient arrivals Feature engineering machine learning algorithms Patient visits forecast Time series forecasting

来源：评论

学校读者我要写书评

暂无评论

On the textile fibre's analysis for forensics, utilizing FTIR spectroscopy and machine learning methods

引用

FORENSIC CHEMISTRY 2024年 39卷

作者： Sharma, Vishal Mahara, Mamta Sharma, Akanksha Panjab Univ Inst Forens Sci & Criminol Chandigarh 160014 India

Fibres are prevalent and can be encountered as trace evidence in various situations. In cases of rape and physical assault, analyzing trace fibre components and assessing their transferability can establish connections between individuals and crime scenes or between perpetrators and victims. This study involved Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) characterization of 104 fibre samples, including natural fibres like cotton and wool (43 samples) and terry wool and synthetic fibres (61 samples). Prominent peaks in different textile fibre spectra were primarily found in the fingerprint region (1800-450 cm-1). To simplify analysis, the spectral data was reduced to principal components, and sample discrimination was performed using Python's PyCaret package. Multiple machine learning algorithms were explored for differentiating fibre samples, and the most effective one was selected for further validation. This study demonstrates the feasibility of developing an ATR-FTIR database for additional textile fibre samples, aiding in the detection of unknown or suspect fibres in the future.

关键词： Textile fibres ATR-FTIR characterization machine learning algorithms PyCaret Forensic analysis

来源：评论

学校读者我要写书评

暂无评论

Identification of ZNF652 as a Diagnostic and Therapeutic Target in Osteoarthritis Using machine learning

引用

JOURNAL OF INFLAMMATION RESEARCH 2024年 17卷 10141-10161页

作者： Chen, Yeping Liang, Rongyuan Zheng, Xifan Fang, Dalang Lu, William W. Chen, Yan Guangxi Med Univ Dept Bone & Joint Surg Affiliated Hosp 1 Nanning Guangxi Peoples R China Youjiang Med Coll Nationalities Dept Thyroid & Breast Surg Affiliated Hosp Baise Guangxi Peoples R China Univ Hong Kong Dept Orthoped & Traumatol Hong Kong Peoples R China

Purpose: Osteoarthritis (OA) is the most common degenerative joint disease. However, its etiology remains largely unknown. Zinc Finger Protein 652 (ZNF652) is a transcription factor implicated in various biological processes. Nevertheless, its role in OA has not been elucidated. Methods: The search term "osteoarthritis" was utilized to procure transcriptome data relating to OA patients and healthy people from the Gene Expression Omnibus (GEO) database. Then a screening process was initiated to identify differentially expressed genes (DEGs). The DEGs were discerned using three distinct machine learning methods. The accuracy of these DEGs in diagnosing OA was evaluated using the Receiver Operating Characteristic (ROC) Curve. A competitive endogenous RNA (ceRNA) visualization network was established to delve into potential regulatory targets. The ZNF652 expression was confirmed in the cartilage of OA rats using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting (WB) and analyzed using an independent t-test. Results: ZNF652 was identified as a DEG and exhibited the highest diagnostic value for OA according to the ROC analysis. The GO and KEGG enrichment analyses suggest that ZNF652 plays a vital role in OA development through processes including nitric oxide anabolism, macrophage proliferation, immune response, and the PI3K/Akt and the MAPK signaling pathways. The increased expression of ZNF652 in OA was validated in qRT-PCR (1.193 +/- 0.005 vs 1.000 +/- 0.005, p < 0.001) and WB (0.981 +/- 0.055 vs 0.856 +/- 0.026, p = 0.012) analysis. Conclusion: ZNF652 was found to be related to OA pathogenesis and can potentially serve as a diagnostic and therapeutic target of OA. The underlying mechanism is that ZNF652 was related to nitric oxide anabolism, macrophage proliferation, various signaling pathways, and immune cells and their functions in OA. Nevertheless, the findings need to be confirmed in clinical trials and the molecular mecha

关键词： osteoarthritis zinc finger protein 652 machine learning algorithms immune cell

来源：评论

学校读者我要写书评

暂无评论

Understanding Loss Landscape Symmetry in Federated learning: Implications for Model Fusion and Optimization

引用

IT PROFESSIONAL 2025年第2期27卷 64-68页

作者： Dubey, Praveer Kumar, Mohit Natl Inst Technol Jalandhar 144011 India

Understanding the optimization landscape in deep neural networks is essential for evaluating model performance, especially in distributed environments like federated learning (FL). Recent studies highlight the importance of valley symmetry in the loss landscape, showing how factors such as dataset heterogeneity, network architecture, and initialization affect convergence. This review delves into these findings within the FL context, stressing the significance of sign consistency in model updates and its impact on model fusion and parameter alignment. Our analysis lays the groundwork for creating more resilient FL systems capable of handling data heterogeneity and asynchronous updates effectively.

关键词： machine learning algorithms Federated learning Reviews Artificial neural networks Network architecture Optimization Context modeling Convergence Performance evaluation

来源：评论

学校读者我要写书评

暂无评论

Sex estimation from coxal bones using deep learning in a population balanced by sex and age

引用

INTERNATIONAL JOURNAL OF LEGAL MEDICINE 2024年第6期138卷 2617-2623页

作者： Epain, Marie Valette, Sebastien Zou, Kaifeng Faisan, Sylvain Heitz, Fabrice Croisille, Pierre Fracasso, Tony Fanton, Laurent Hosp Civils Lyon Edouard Herriot Hosp Serv Legal Med Lyon France Claude Bernard Lyon 1 Univ Univ Lyon Lyon Est Med Sch Lyon France INSA Lyon INSERM U630 CREATIS LRMN Lyon France CNRS Lab Sci Ingenieur Informat & Imagerie UMR 7357 ICubeUNISTRA F-67412 Illkirch Graffenstaden France Univ Hosp St Etienne Radiol Dept St Etienne France Univ Geneva Ctr Univ romand medecine legale Dept med commun Geneva Switzerland

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (Crecon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + Crecon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology.

关键词： Forensic anthropology Sexual dimorphism Sex prediction machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study

引用

BRITISH JOURNAL OF HAEMATOLOGY 2024年第6期204卷 2405-2417页

作者： Li, Yang Sun, Ting Chen, Jia Liu, Xiaofan Fu, Rongfeng Xue, Feng Liu, Wei Ju, Mankai Dai, Xinyue Li, Huiyuan Wang, Wentian Chi, Ying Li, Ting Shao, Shuai Yang, Renchi Chen, Yunfei Zhang, Lei Chinese Acad Med Sci Inst Hematol & Blood Dis Hosp Peking Union Med Coll Natl Clin Res Ctr Blood Dis Tianjin Peoples R China Tianjin Inst Hlth Sci Tianjin Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Populat Med & Publ Hlth Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Hematol & Blood Dis Hosp Natl Clin Res Ctr Blood Dis State Key Lab Expt HematolHaihe Lab Cell Ecosyst Tianjin 300020 Peoples R China

Immune thrombocytopenia (ITP) is an autoimmune disease characterized by antibody-mediated platelet destruction and impaired platelet production. The mechanisms underlying ITP and biomarkers predicting the response of drug treatments are elusive. We performed a metabolomic profiling of bone marrow biopsy samples collected from ITP patients admission in a prospective study of the National Longitudinal Cohort of Hematological Diseases. machine learning algorithms were conducted to discover novel biomarkers to predict ITP patient treatment responses. From the bone marrow biopsies of 91 ITP patients, we quantified a total of 4494 metabolites, including 1456 metabolites in the positive mode and 3038 metabolites in the negative mode. Metabolic patterns varied significantly between groups of newly diagnosed and chronic ITP, with a total of 876 differential metabolites involved in 181 unique metabolic pathways. Enrichment factors and p-values revealed the top metabolically enriched pathways to be sphingolipid metabolism, the sphingolipid signalling pathway, ubiquinone and other terpenoid-quinone biosynthesis, thiamine metabolism, tryptophan metabolism and cofactors biosynthesis, the phospholipase D signalling pathway and the phosphatidylinositol signalling system. Based on patient responses to five treatment options, we screened several metabolites using the Boruta algorithm and ranked their importance using the random forest algorithm. Lipids and their metabolism, including long-chain fatty acids, oxidized lipids, glycerophospholipids, phosphatidylcholine and phosphatidylethanolamine biosynthesis, helped differentiate drug treatment responses. In conclusion, this study revealed metabolic alterations associated with ITP in bone marrow supernatants and a potential biomarker predicting the response to ITP.

关键词： bone marrow microenvironment immune thrombocytopenia machine learning algorithms metabolomics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：