Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…Compared with previous studies, a more stable water content prediction model of Anshan magnetite was constructed by combining data preprocessing, CARS feature screening and nonlinear regression algorithm, which provides higher precision support for water content detection in mining production.…”
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Development and Validation of Early Alert Model for Diabetes Mellitus–Tuberculosis Comorbidity by Zhaoyang Ye, Guangliang Bai, Ling Yang, Li Zhuang, Linsheng Li, Yufeng Li, Ruizi Ni, Yajing An, Liang Wang, Wenping Gong

“…This study identified three potential immune-related biomarkers for DM–TB, and the constructed risk assessment model demonstrated significant predictive efficiency, providing an early screening strategy for DM–TB.…”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The models were applied to be constructed in R-project (version 3.5.2) and the ‘caret’ package was applied to tune the machine learning algorithm parameters. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. …”
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Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis by Jian Du, Tian Zhou, Wei Zhang, Wei Peng

“…Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). …”
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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. The random forest algorithm was applied to select important variables based on routine blood tests, and a random forest (RF) model was constructed to predict the efficacy and prognosis of ICIs treatment. …”
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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

“…Shapley Additive exPlanations method was used to interpret the model predictions. Results We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. …”
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Immunogenic cell death genes in single-cell and transcriptome analyses perspectives from a prognostic model of cervical cancer by Li Ning, Li Ning, Xiu Li, Xiu Li, Yating Xu, Yating Xu, Yu Si, Yu Si, Hongting Zhao, Qinling Ren, Qinling Ren

“…This study sought to investigate the significance of ICD in CESC and to establish an ICDRs prognostic model to improve immunotherapy efficacy for patients with cervical cancer.MethodsICD-associated genes were screened at the single-cell and transcriptome levels based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. …”
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Model OLD0: A Physical Parameterization for Clear-Sky Downward Longwave Radiation by Juan Carlos Ceballos, Diego Pereira Enoré, Jaidete Monteiro de Souza, Francisco Luiz Leitão de Mesquita

“…In contrast, other widely used algorithms typically exhibit |MBEs| ranging from 8.1 to 15.9 W.m-2.…”
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All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning by Min A, Liu Y, Fu M, Hou Z, Wang Z

“…Cox proportional hazards regression is used to explore the association between fractures type and mortality. Boruta algorithm was used to screen the risk factors related to death. …”
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A cost-utility analysis of newborn screening for spinal muscular atrophy in Canada by Alex Pace, Weston Roda, Corrina Poon, Hugh J. McMillan, Maryam Oskoui, Alex MacKenzie, Pranesh Chakraborty, Jeff Round

“…Methods A decision analytic model was developed, which combined a decision tree for the screening algorithm and a Markov model for long-term health outcomes. …”
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Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a p... by Zhe Xu, Qiuhan Chen, Zhi Zhou, Jianbo Sun, Guang Tian, Chen Liu, Guangzhi Hou, Ruguo Zhang

“…The purpose of this study was to screen risk factors for the intraoperative V-effect in intertrochanteric fractures and to develop a clinical prediction model. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

“…To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. …”
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Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population by Alexander Bremner, Li Yen Chan, Courtney Jones, Shaheen P. Shah

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Accelerating structure relaxation in chemically disordered materials with a chemistry-driven model by Liying An, Huan Ma, Jinjia Liu, Wenping Guo, Xiaodong Wen

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A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy by Sebastian Dinesen, Marianne G. Schou, Christoffer V. Hedegaard, Yousif Subhi, Thiusius R. Savarimuthu, Tunde Peto, Jakob K. H. Andersen, Jakob Grauslund

“…We then applied our pre-established DL segmentation model to annotate nine lesion types before training the algorithm. …”
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Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation by Kendrick Matthew Shaw, Yu-Ping Shao, Manohar Ghanta, Valdery Moura Junior, Eyal Y Kimchi, Timothy T Houle, Oluwaseun Akeju, Michael Brandon Westover

“…Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention. ObjectiveThis study aims to develop a machine learning algorithm to identify patients at the highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening. …”
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Intelligence model-driven multi-stress adaptive reliability enhancement testing technology by Shouqing Huang, Beichen He, Jing Wang, Xiaoyang Li, Rui Kang, Fangyong Li

“…In addition, we propose a three-factor step-by-step screening algorithm and scoring model to determine the optimal sequential test points. …”
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