Prediction model for the selection of patients with glioma to proton therapy by Jesper Folsted Kallehauge, Siri Grondahl, Camilla Skinnerup Byskov, Morten Høyer, Slavka Lukacova

“…Univariate and multivariate logistic regression were used to assess the association with selection for PT. The dataset was split into training (n = 37, period 2019–2022) and test (n = 12, period 2023) cohorts. …”
Get full text

Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retr... by Hongyi Li, Cancan Chang, Bo Zhou, Yu Lan, Peizhuo Zang, Shannan Chen, Shouliang Qi, Ronghui Ju, Yang Duan

“…An APCV-DMV radiomic model was created via the SVM algorithm, and independent clinical risk factors associated with AIS were combined with the radiomic model to generate a joint model. …”
Get full text

Crop classification with deep convolutional neural network based on crop feature by Mohamad Reza Gili, Davoud Ashourloo, Hosein Aghighi, Ali Akbar Matkan, Alireza SHakiba

“…Due to the spectral overlap of the crops in some time periods, network training was associated with a relatively high loss and therefore, for the test area, the overall classification accuracy was 69% (percent) and the kappa coefficient was 0.55. …”
Get full text

Effectiveness of biomarker-guided duration of antibiotic treatment in children hospitalised with confirmed or suspected bacterial infection: the BATCH RCT by Cherry-Ann Waldron, Philip Pallmann, Simon Schoenbuchner, Debbie Harris, Lucy Brookes-Howell, Céu Mateus, Jolanta Bernatoniene, Katrina Cathie, Saul N Faust, Josie Henley, Lucy Hinds, Kerry Hood, Chao Huang, Sarah Jones, Sarah Kotecha, Sarah Milosevic, Helen Nabwera, Sanjay Patel, Stéphane Paulus, Colin VE Powell, Jenny Preston, Huasheng Xiang, Emma Thomas-Jones, Enitan D Carrol

“…In the presence of robust antimicrobial stewardship programmes to reduce antibiotic use, a procalcitonin-guided algorithm may offer little added value. Future work Future trials must include an implementation framework to improve trial intervention fidelity, and repeated cycles of education and training to facilitate implementation of biomarker-guided algorithms into routine clinical care. …”
Get full text

Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study by Yuan Gui, Wei Hu, Jialiang Ren, Fuqiang Tang, Limei Wang, Fang Zhang, Jing Zhang

“…Thus, univariate logistic regression, correlation analysis, and the Boruta algorithm were applied for further feature dimension reduction, selecting radiomics and DL features highly associated with meningioma sinus invasion. …”
Get full text

Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia by Fangmin Zhong, Fangyi Yao, Zihao Wang, Jing Liu, Bo Huang, Xiaozhong Wang

“…The optimal risk score model was selected by employing the C-index as the criterion on the basis of training 10 machine learning algorithms on 9 multicenter AML cohorts. …”
Get full text

Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes. by Min-Xiao Wang, Chang-Sheng Liao, Xue-Qin Wei, Yu-Qin Xie, Peng-Fei Han, Yan-Hui Yu

“…To ensure data consistency and comparability, we standardized the training sets and removed batch effects using the ComBat algorithm, thereby integrating them into a unified gene expression dataset. …”
Get full text

Development and Validation of a Machine Learning Model for Predicting Long-Term Depression Risk in ACS Patients After PCI: A Retrospective Cohort Study by Lv H, Sun F, Zhang Y, Zhou X

“…Feature selection was conducted using the Boruta algorithm, and restricted cubic spline (RCS) analysis was applied to assess non-linear associations. …”
Get full text

Informing antimicrobial stewardship with explainable AI. by Massimo Cavallaro, Ed Moran, Benjamin Collyer, Noel D McCarthy, Christopher Green, Matt J Keeling

“…We considered a data set of hospital admissions linked to records of antibiotic prescriptions and susceptibilities of bacterial isolates. An appropriately trained gradient boosted decision tree algorithm, supplemented by a Shapley explanation model, predicts the likely antimicrobial drug resistance, with the odds of resistance informed by characteristics of the patient, admission data, and historical drug treatments and culture test results. …”
Get full text

Meteorological and satellite-based data for drought prediction using data-driven model by ALI H. AHMED SULIMAN

Get full text

Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement by Tahmineh Adili, Zohreh Rostamnezhad, Ali Chaibakhsh, Ali Jamali

“…For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. …”
Get full text

Analysis of machine learning approaches for the interpretation of acoustic fields obtained by well noise data modelling by N. V. Mutovkin

“…Data sets for training and testing the algorithm were obtained on the basis of scenarios calculated using a two-dimensional mathematical model with the different values of the bed parameters and ratio of volume fractions of the well filling fluids. …”
Get full text

Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer by Rui Zhang, Chunfan Jiang, Feng Li, Lin Li, Xiaomin Qin, Jiang Yang, Huabing Lv, Tao Ai, Lei Deng, Chencui Huang, Hui Xing, Feng Wu

“…The SVM algorithm achieved the highest predictive performance for CD3 T-cell expression status, with an area under the curve (AUC) of 0.93 in the training group and 0.92 in the test group. …”
Get full text

Emergency Medical Care for Victims with Crush Syndrome at the Pre-Hospital Stage by V. Orlyk

“…Implementing these measures can significantly reduce mortality from injuries and complications associated with crush syndrome. The informative materials outlined in this article align with continuing medical education programs and training curricula for medical students, interns, and master's students in higher medical education institutions. …”
Get full text

A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study by Lingfeng Zhang, Gang Xie, Yue Zhang, Yue Zhang, Junlin Li, Junlin Li, Wuli Tang, Wuli Tang, Ling Yang, Ling Yang, Kang Li

“…Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs).ResultsLogistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. In the training and validation cohorts, the AUCs of clinical model were 0.836 and 0.773, respectively, for differentiating MCE patients; the AUCs of radiomics model were 0.849 and 0.818, respectively; the AUCs of clinical and radiomics model were 0.912 and 0.916, respectively.ConclusionThis model can assist in predicting MCE after acute ischemic stroke and can provide guidance for clinical treatment and prognostic assessment.…”
Get full text

Preparedness of European pediatric oncologists to integrate AI in the clinical routine by Alberto E. Tozzi, Diana Ferro, Ileana Croci, Francesco Fabozzi, Angela Mastronuzzi

“…Discussion: This survey underscores the importance of AI tools in pediatric oncology that incorporate human oversight in clinical decision-making and training AI algorithms with diverse and representative data. …”
Get full text

Application of Fourier transform infrared spectroscopy to exhaled breath analysis for detecting helicobacter pylori infection by Meiqi Qiu, Fei Liao, Yulin Tan, Junlong Zhang, Changjun Zheng, Hanyu Wang, Huangming Zhuang, Wanli Xiong, Qingfang Xie, Weiguo Dong

“…Individual exhalation spectral data after deducting baseline spectral data were used as the basis for the training and test sets through K-center clustering algorithm. …”
Get full text

Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Abstract Study objectives This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model’s predictions. …”
Get full text

Using Machine Learning to Understand Injuries in Female Agricultural Operators in the Central United States by Cheryl L. Beseler, Risto H. Rautiainen

“…In this study, we used XGBoost, a machine learning algorithm, and logistic regression to examine 17 factors hypothesized to be associated with injury in 1529 farm and ranch women. …”
Get full text

Interpretable XGBoost model identifies idiopathic central precocious puberty in girls using four clinical and imaging features by Lu Tian, Yan Zeng, Helin Zheng, Jinhua Cai

“…The least absolute shrinkage and selection operator (LASSO) method was used to select essential characteristic parameters associated with ICPP and were used to construct logistic regression (LR) and five machine learning (ML) models, including support vector machine (SVM), Gaussian naive bayes (GaussianNB), extreme gradient boosting (XGBoost), random forest (RF), and k- nearest neighbor algorithm (kNN). …”
Get full text