Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus by Qipeng Wei, Zihao Liu, Xiaofeng Chen, Hao Li, Weijun Guo, Qingyan Huang, Jinxiang Zhan, Shiji Chen, Dongling Cai, Dongling Cai

“…Potential predictive features were identified using univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Eight supervised ML algorithms were applied to construct predictive models. …”
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Research Trends on Metabolic Syndrome in Digital Health Care Using Topic Modeling: Systematic Search of Abstracts by Kiseong Lee, Yoongi Chung, Ji-Su Kim

“…The methodological approach included text preprocessing, text network analysis, and topic modeling using the BERTopic algorithm. …”
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AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. …”
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Remote Sensing for Urban Biodiversity: A Review and Meta-Analysis by Michele Finizio, Federica Pontieri, Chiara Bottaro, Mirko Di Febbraro, Michele Innangi, Giovanna Sona, Maria Laura Carranza

“…Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). …”
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Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast by Yuan Qi, Tan Liu, Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao, Tongyu Xu

“…The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. …”
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Design and refinement of a clinical trial staffing model within the evolving landscape of oncology clinical trials by Ellen Siglinsky, Hannah Phan, Silviya Meletath, Amber Neal, David E. Gerber, Asal Rahimi, Erin L. Williams

“…We developed and evaluated a staffing model designed to meet this need. Methods: To address individual protocol acuity, the model's algorithms include metrics to account for visit frequency, and the quantity, and types of research-related procedures. …”
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Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…Background With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and prognosis determination.Methods Here, we conducted a systematic search of the PubMed, Cochrane Library, Embase (Ovid), Web of Science, and Scopus databases on April 28, 2023, and screened relevant literature. …”
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Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding by Yani Zhang, Qiankun Li, Haijun Duan, Liang Tan, Ying Cao, Junxin Chen

“…This study involved a large cohort of 56,878 hospitalized patients, and we leveraged the XGBoost algorithm to establish a predictive model based on these features. …”
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth by Rui Zhou, Ziqian Liu, Tongtong Wu, Xianwei Pan, Tongtong Li, Kaiting Miao, Yuru Li, Xiaohui Hu, Haigang Wu, Andrew M. Hemmings, Beier Jiang, Zhenzhen Zhang, Ning Liu

“…Identification of new selective EGFR-T790M inhibitors has proven challenging through traditional screening platforms. With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency. …”
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Oxidative stress-related genes in uveal melanoma: the role of CALM1 in modulating oxidative stress and apoptosis and its prognostic significance by Yue Wu, Xiaoyan Cai, Menghan Hu, Runyan Cao, Yong Wang

“…Protein–protein interaction (PPI) networks were constructed to identify hub genes, and machine learning algorithms were utilized to screen for diagnostic genes, employing methods such as least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine (SVM), gradient boosting machine (GBM), neural network algorithm (NNET), and eXtreme gradient boosting (XGBoost). …”
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Civil Aircraft Landing Attitude Ultra-Limit Warning System Based on mRMR-LSTM by Fei Lu, Tong Jing, Chunsheng Xie, Haonan Chen

“…Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. …”
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Construction and validation of a machine learning based prognostic prediction model for children with traumatic brain injury by Yongwei Wei, Jiandong Wang, Yu Su, Fan Zhou, Huaili Wang

“…Then, the risk scores and other indicators were used to construct an extended prediction model through the extreme gradient boosting (XGBoost) algorithm. …”
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Utilizing machine learning models for predicting outcomes in acute pancreatitis: development and validation in three retrospective cohorts by Kaier Gu, Yang Liu

“…Six ML algorithms were employed to construct predictive models. …”
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Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes by Liu Yang, Li Du, Yuanyuan Ge, Muhui Ou, Wanyan Huang, Xianmei Wang

“…Logistic Regression was used to screen for factors that were significant for ML model establishment. …”
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Parameter Sensitivity Analysis and Irrigation Regime Optimization for Jujube Trees in Arid Regions Using the WOFOST Model by Shihao Sun, Yingjie Ma, Pengrui Ai, Ming Hong, Zhenghu Ma

“…In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. …”
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Development and Validation of the Promising PPAR Signaling Pathway-Based Prognostic Prediction Model in Uterine Cervical Cancer by Yan Zhang, Xing Li, Jun Zhang, Lin Mao, Zou Wen, Mingliang Cao, Xuefeng Mu

“…Furthermore, cervical cancer patients with different PPAR scores show different sensitivity to immune checkpoint therapy. In order to screen the genes to serve as the best biomarker for cervical cancer patients, we then construct the PPAR-based prognostic prediction model. …”
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Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer by Ji Eun Baek, Hahn Yi, Seung Wook Hong, Subin Song, Ji Young Lee, Sung Wook Hwang, Sang Hyoung Park, Dong-Hoon Yang, Byong Duk Ye, Seung-Jae Myung, Suk-Kyun Yang, Namkug Kim, Jeong-Sik Byeon

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Systematic Construction and Validation of a Novel Ferroptosis-Related Gene Model for Predicting Prognosis in Cervical Cancer by Wentao Qin, Can He, Daqiong Jiang, Yang Gao, Yu Chen, Min Su, Yuanjun Yang, Zhao Yang, Hongbing Cai, Hua Wang

“…The prediction model was verified by the nomogram integrating clinical characteristics; the GSE44001 dataset was used as an external verification. …”
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A web-based prediction model for brain metastasis in non-small cell lung cancer patients by Jianing Chen, Li Wang, Li Liu, Qi Wang, Jing Zhao, Xin Yu, Shiji Zhang, Chunxia Su

“…Subsequently, seven machine learning models were constructed employing diverse algorithms, namely Logistic Regression (LR), Classification and Regression Tree (CART), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGBOOST). …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…The dataset was split into training and testing sets (7:3 ratio), and seven machine learning models, including the ensemble Super Learner, were trained. …”
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