Clinical performances of EuroSCORE II risk stratification model in Serbian cardiac surgical population: A single centre validation study including 10,048 patients by Nežić Duško, Raguš Tatjana, Mićović Slobodan, Trajić Snežana, Spasojević-Milin Biljana, Petrović Ivana, Košević Dragana, Borzanović Milorad

“…The EuroSCORE II has recently been developed with an idea to provide better accuracy in prediction of perioperative mortality in the patients who underwent open heart surgery. …”
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A predictive model for WHO/ISUP pathologic grading of renal clear cell carcinoma based on CT radiomics: a multicenter study by Chunying Wu, Yuzhen Xi, Juanjuan Hu, Guodong Li, Xu Wang, Xiaofei Jiao, Zhongxiang Ding, Weiying Sun

“…Abstract Objective This study aims to evaluate the predictive value of CT radiomics combined with clinical-imaging features for the WHO/ISUP pathological grade of clear cell renal cell carcinoma(ccRCC). …”
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Development and validation of a nomogram to predict bacterial blood stream infection by Yu Huan Jiang, Rui Zhao, Yun Xue Bai, Hui Ming Li, Jun Liu, Shi Xuan Wang, Xing Xie, Yang Liu, Qiang Chen

“…Abstract Objective To identify the risk factors of bacterial blood stream infection (BSI) and construct a nomogram to predict the occurrence of bacterial BSI. Methods Blood stream infection is characterized by a systemic infection patient with positive blood culture and has one or more clinical symptoms, such as fever (body temperature > 38 °C) or hypothermia (body temperature < 36 °C), chills, hypotension, oliguria, or high lactic acid levels. …”
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Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis by Menghui He, Zhongsheng Lu, Yiwei Lv, Zihai Cheng, Qiang Zhang, Xiaoqing Jin, Pei Han

“…This study compared the predictive efficacy of multiple machine learning models to identify the optimal model for forecasting long-term prognosis in HICH patients.MethodsWe conducted a retrospective analysis of clinical data from 807 HICH patients admitted to Qinghai Provincial People's Hospital's Neurosurgery Department between June 2020 and June 2024. …”
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Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning by Bingkun Zhou, Hu Zhou, Xiaodong Huang, Shijie Liu

“…Besides routine laboratory indicators and medical history, risk prediction models can predict CKD outcome. However, there is currently a lack of CKD prognostic prediction models based on transcriptomics and machine learning.MethodsUtilizing weighted correlation network analysis (WGCNA) and random forest algorithms in GSE137570, three core gene sets of different sizes were constructed, which were externally validated in GSE66494 and GSE180394, and evaluated for their predictive performance in GSE45980 by receiver operating characteristic (ROC) curves. …”
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…Abstract Background Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. …”
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Development and validation of prediction model for vitamin D deficiency in Chinese college students (a dynamic online nomogram predicting vitamin D deficiency for Chinese college s... by Yingyi Luo, Chunbo Qu, Guyanan Li, Qiannan Di, Shangzhen Ding, Ruoyou Jiang, Ruotong Wang, Siyuan Wang, Lixin Na

“…Abstract Objective This study aims to develop a model for predicting vitamin D deficiency in Chinese college students using easily accessible clinical characteristics. …”
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A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy by Jiaqi Wang, Kangjing Xu, Changsheng Zhou, Xinbo Wang, Junbo Zuo, Chenghao Zeng, Pinwen Zhou, Xuejin Gao, Li Zhang, Xinying Wang

“…This study aimed to construct and validate a model for predicting postoperative major complications in patients after PD. …”
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Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures by Mohsin Ali, Li Chen, Qadir Bux Alias Imran Latif Qureshi, Deema Mohammed Alsekait, Adil Khan, Kiran Arif, Muhammad Luqman, Diaa Salama Abd Elminaam, Amir Hamza, Majid Khan

“…Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. …”
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Predictive value of the FIB-4 index, APRI, ALBI score, and GPR for overall survival in treatment-naïve metastatic colorectal cancer patients by Mehmet Serdar Yıldırım, Yunus Güzel, Canan Can, İhsan Kaplan, Veysi Şenses, İhsan Solmaz, Bilgin Bahadır Başgöz, Ömer Faruk Alakuş, Serdar İleri, Halil Kömek

“…The clinical importance of these scores for survival outcomes was evaluated via the Cox regression model, Kaplan–Meier method, and log-rank test. …”
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Development of a nomogram for predicting recurrence of epithelial ovarian cancer involving traditional Chinese medicine treatment by Xiaofeng Chen, Xiaofeng Chen, Xudong Hu, Huanmei Lin, Ziang Li, Baijun Gao, Hongmei Ouyang, Xiangdan Hu, Jing Xiao

“…Our study aims to develop a predictive model for early recurrence of ovarian cancer incorporating Traditional Chinese Medicine (TCM) treatment.MethodsWe reviewed the clinicopathological and prognostic data of EOC patients who achieved complete clinical remission after surgery and chemotherapy at Guangdong Traditional Chinese Medicine Hospital (GPHCM) between December 2011 and July 2022. …”
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Identification of CD19+B Cell as a Diagnostic Biomarker in Sepsis-Induced ARDS by Fu X, Su J, Li X, Zhang C, Yu X

“…However, there is still no effective biomarker to predict the risk and outcome of ARDS induced by sepsis.Methods: In this research, the GSE32707 dataset was acquired from the Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs). …”
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Development and Validation of a Nomogram-Based Risk Prediction Model for Diabetic Retinopathy in Elderly Adults with Type 2 Diabetes Mellitus by Chen M, Niu T, Sun Y, Chang M, Liu S, Xu T, Cui H

“…Calibration plots demonstrated strong agreement between predicted and observed risks (H-L test: p = 0.807 [derivation], p = 0.374 [validation]). …”
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Development and Validation of a Neonatal Hypothermia Prediction Model for In-Hospital Transport Using Machine Learning Algorithms: A Single-Center Retrospective Study by Zhang W, Gu X, Gu C, Yao L, Zhang Y, Wang K

“…Six machine learning algorithms—Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naive Bayes (NB)—were used to develop predictive models. The effectiveness was evaluated using area under the ROC curve (AUC), along with F1 score, accuracy, sensitivity, specificity, and Hosmer-Lemeshow calibration tests with Brier scores. …”
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Radiomic-based models are able to predict the pathologic response to different neoadjuvant chemotherapy regimens in patients with gastric and gastroesophageal cancer: a cohort stud... by Annamaria Agnes, Luca Boldrini, Federica Perillo, Huong Elena Tran, Maria Gabriella Brizi, Riccardo Ricci, Jacopo Lenkowicz, Claudio Votta, Alberto Biondi, Riccardo Manfredi, Vincenzo Valentini, Domenico M. D’Ugo, Roberto Persiani

“…Radiomic models (in the entire case series and according to NAC regimens) were evaluated using the receiver operating characteristic area under the curve (AUC), sensitivity, and negative predictive value (NPV). …”
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Parallelizing process model integration for model predictive control through oracle design and analysis for a Grover’s algorithm-inspired optimization strategy by Kip Nieman, Helen Durand, Saahil Patel, Daniel Koch, Paul M. Alsing

“…In one method of solving this problem, the time required to find the optimal values of the decision variables depends on the time required to perform the arithmetic operations involved in computing the model predictions. …”
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Mapping soil drainage classes: Comparing expert knowledge and machine learning strategies by Danilo César de Mello, Nélida E.Q. Silvero, Bradley A. Miller, Nicolas Augusto Rosin, Jorge Tadeu Fim Rosas, Bruno dos Anjos Bartsch, Gustavo Vieira Veloso, Jean Jesus Macedo Novais, Renan Falcioni, Marcos Rafael Nanni, Marcelo Rodrigo Alves, Elpídio Inácio Fernandes-Filho, Uemeson José dos Santos, José Alexandre Melo Demattê

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Prediction of tumor deposits in stage I-III gastric cancer: a clinically applicable nomogram integrating clinicopathology outcomes by Kunjie Wang, Yue Huo, Yuanfang Zhang, Song Guo, Weiguang Yu, Na Xiao, Shenyong Su, Lin An

“…ObjectiveThis study seeks to identify clinicopathological risk factors associated with tumor deposits (TD) development in stage I-III gastric cancer patients and to construct a visualized predictive model for clinical application.MethodsA retrospective cohort of 1,284 gastric cancer patients treated at the Affiliated Hospital of Hebei University (September 2010–September 2022) was analyzed. …”
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Study on prediction model and influencing factors of progression-free survival in colorectal cancer by CHEN Jiaying, CHU Yimin, PENG Haixia

“…The included clinical data were analyzed using univariate and multivariate Cox proportional hazards models to explore independent factors affecting postoperative PFS in patients with CRC and to establish a clinical prognostic prediction model based on these factors. The discrimination and calibration of the prediction model were evaluated by using concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and survival curve. …”
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Efficacy and validation of a clinical predictive model for chronic atrophic gastritis in patients: a multi-center retrospective analysis by Xiang Fang, Wenjing Ding, Xiaolong Xu, Hui Chen, Bei Pei, Yi Zhang, Biao Song, Xuejun Li, Li Yao

“…This study aimed to identify risk factors and construct a predictive model for better diagnosis of CAG.MethodsWe utilized a multi-center retrospective analysis, including 539 cases of CAG patients diagnosed and treated in Second Affiliated Hospital of Anhui University of Chinese Medicine from September 2018 to December 2024 as training dataset, and 230 clinical data diagnosed with CAG from Hefei Second People’s Hospital from April 2018 to November 2024 as validation dataset to establish the predictive model. …”
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