Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients by Wenwei Zuo, Xuelian Yang

“…In addition, the prediction results of the XGBoost model were interpreted in detail using the SHAP algorithm. …”
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Risk Factors and Predictive Model for Ischemic Complications in Endovascular Treatment of Intracranial Aneurysms: Insights From a Large Patient Cohort by Jianwen Jia, Zeping Jin, Mirzat Turhon, Yixin Lin, Xinjian Yang, Yang Wang, Yunpeng Liu

“…A total of five potential factors were screened using LASSO regression, XGBoost, and Randomforest algorithms: hypertension, history of alcohol consumption, multiple IAs, rupture status, and antiplatelet agent. …”
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Experimental investigations and field applications of a tension estimation method for two linked suspenders using only local vibration measurements by Wen-Hwa Wu, Chien-Chou Chen, Meng-Xuan Wu, Gwolong Lai

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Toward Adaptive and Immune-Inspired Viable Supply Chains: A PRISMA Systematic Review of Mathematical Modeling Trends by Andrés Polo, Daniel Morillo-Torres, John Willmer Escobar

“…At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. …”
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RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks. by Souvik Seal, Qunhua Li, Elle Butler Basner, Laura M Saba, Katerina Kechris

“…We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. …”
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Comparative Analysis of Osteoarthritis Therapeutics: A Justification for Harnessing Retrospective Strategies via an Inverted Pyramid Model Approach by Quinn T. Ehlen, Jacob Jahn, Ryan C. Rizk, Thomas M. Best

“…In comparison to the prospective approach, the retrospective strategy is likely more cost-effective, more widely applicable, and does not necessitate thorough and invasive genetic screening. …”
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Screening for More than 1,000 Pesticides and Environmental Contaminants in Cannabis by GC/Q-TOF by Philip L. Wylie, Jessica Westland, Mei Wang, Mohamed M. Radwan, Chandrani G. Majumdar, Mahmoud A. ElSohly

“… A method has been developed to screen cannabis extracts for more than 1,000 pesticides and environmental pollutants using a gas chromatograph coupled to a high-resolution accurate mass quadrupole time-of-flight mass spectrometer (GC/Q-TOF). …”
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Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data by Xiaoyu Yang, Jinjian Xu, Hong Ji, Jun Li, Bingqing Yang, Liye Wang

“…Several classical machine learning algorithms were applied in combination with the BGE-M3 large-language model (LLM) for enhanced semantic feature extraction. …”
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AI-Based Prediction of Visual Performance in Rhythmic Gymnasts Using Eye-Tracking Data and Decision Tree Models by Ricardo Bernardez-Vilaboa, F. Javier Povedano-Montero, José Ramon Trillo, Alicia Ruiz-Pomeda, Gema Martínez-Florentín, Juan E. Cedrún-Sánchez

“…Conclusion: The decision tree algorithm achieved the highest performance in predicting short-term fixation stability, but its effectiveness was limited in tasks involving accommodative facility, where other models such as SVM and KNN outperformed it in specific metrics. …”
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NMD-FusionNet: a multimodal fusion-based medical imaging-assisted diagnostic model for liver cancer by Qing Ye, Minghao Luo, Jing Zhou, Chunlei Cheng, Lin Peng, Jia Wu

“…The framework includes a three-stage pipeline: first, a refined non-local means filtering algorithm is employed for pre-screening, discarding over 80% of non-diagnostic images using adaptive thresholding; second, a multimodal image fusion method integrates multi-phase, multi-source liver cancer image data through multi-scale decomposition and precise fusion rules to reduce noise and motion artifacts; third, a dual-path DconnNet segmentation network is constructed, incorporating a directional excitation module in the encoder and a spatial awareness unit in the decoder, guided by a boundary-constrained loss function to enhance segmentation accuracy. …”
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Collaborative governance model for spoil disposal and gully infill land creation near open-pit coal mines by Shaogang LEI, Jianying ZHANG, Chang LIU, Liang WANG, Zhenwang JIA

“…The main technical steps include: extracting the location of the gully to be treated based on the algorithm of constructing concentric rectangular windows inside and outside, optimizing the earthwork allocation path of the waste dump based on the “source sink” theory, backfilling the gully area based on the reshaping of the near natural landform, screening the waste materials and reconstructing the soil layer profile of the gully backfilling, greening and land reuse of the covering soil, and evaluating the ecological effects of collaborative mining and treatment. …”
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Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs by Chen Zihang, Liu Liang, Su Qianmin, Cheng Gaoyi, Huang Jihan, Li Ying

“…However, recent years have seen the evolution of knowledge graphs and the introduction of large language models (LLMs), providing innovative approaches for the pre-screening and recruitment phases of clinical trials. …”
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ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings by Qiong Yao, Qiong Yao, Pan Pan, Pan Pan, Xiaoming Zheng, Xiaoming Zheng, Guomin Zhou, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model’s ability to represent target features and detection accuracy.ResultsThe experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%.ConclusionThe experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.…”
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Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation by Yimin Zhou, Xin Li, Zixiu Wang, Liqi Ng, Rong He, Chaozong Liu, Gang Liu, Xiao Fan, Xiaohong Mu, Yu Zhou, Yu Zhou

“…Three machine learning models (RF, LASSO, and SVM) were constructed to screen candidate genes, and a Nomogram model was used for verification. …”
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Machine learning models in enhancing prediction of health-related indices among older adults: A scoping review by Raoof Nopour

“…Objective: This scoping review aims to investigate machine learning models in predicting health-related indices among older adults. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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A Computationally Efficient Model Predictive Control Energy Management Strategy for Hybrid Vehicles Considering Driving Style by Yalian Yang, Yuqi Chen, Changdong Liu

“…The driving-style adaptive Pontryagin’s minimum principle for model predictive control (DSA-PMP-MPC) algorithm was designed as a real-time energy management strategy for Hybrid Electric Vehicles (HEVs). …”
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Multi-Comparison of Different Ocular Imaging Modality-based Deep Learning Models for Visually Significant Cataract Detection by Jocelyn Hui Lin Goh, BEng, Xiaofeng Lei, MSc, Miao-Li Chee, MPH, Yiming Qian, PhD, Marco Yu, PhD, Tyler Hyungtaek Rim, MD, PhD, Simon Nusinovici, PhD, David Ziyou Chen, MBBS, FRCOphth, Kai Hui Koh, BSc, Samantha Min Er Yew, BSc, Yibing Chen, BEng, Victor Teck Chang Koh, MBBS, MMed, Charumathi Sabanayagam, MD, PhD, Tien Yin Wong, MD, PhD, Xinxing Xu, PhD, Rick Siow Mong Goh, PhD, Yong Liu, PhD, Ching-Yu Cheng, MD, PhD, Yih-Chung Tham, PhD

“…A community study data set of nonmydriatic retinal photos (N = 310 eyes) was used for external testing of the retinal model. Methods: We developed 3 single-modality DL models (retinal, slit beam, and diffuse anterior segment photos) and 4 ensemble models (4 different combinations of the 3 single-modality models) to detect visually significant cataract (VSC). …”
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An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes by Na An, Zhongwen Lu, Yang Li, Bing Yang, Shaozhen Ji, Xu Dong, Zhaoliang Ding

“…To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. …”
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Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application by Lixia Kuang, Lixia Kuang, Jingya Yu, Yunyu Zhou, Yu Zhang, Yu Zhang, Guangman Wang, Guangman Wang, Fangmin Zhang, Grace Paka Lubamba, Grace Paka Lubamba, Xiaoqin Bi, Xiaoqin Bi

“…The dataset was divided into a training set (70%) and a validation set (30%). Predictive models were developed via four supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost). …”
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