Identification of Alzheimer’s disease biomarkers and their immune function characterization by Mingkai Lin, Yue Zhou, Peixian Liang, Ruoyi Zheng, Minwei Du, Xintong Ke, Wenjing Zhang, Pei Shang

“…Material and methods Based on bulk RNA-seq (GSE122063 and GSE97760), we screened potential biomarkers for AD by differential expression analysis and machine learning algorithms. …”
Get full text

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

Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

LatentDE: latent-based directed evolution for protein sequence design by Thanh V T Tran, Nhat Khang Ngo, Viet Thanh Duy Nguyen, Truong-Son Hy

“…To mitigate this extensive procedure, recent advancements in machine learning-guided methodologies center around the establishment of a surrogate sequence-function model. In this paper, we propose latent-based DE (LDE), an evolutionary algorithm designed to prioritize the exploration of high-fitness mutants in the latent space. …”
Get full text

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. …”
Get full text

Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization by Fadiyah M. Almutairi, Sara A. Althubiti, Shabnam Mohamed Aslam, Habib Dhahri, Omar Alhajlah, Nitin Mittal

“…Various stages in the DL tool comprise; (i) Image collection and resizing, (ii) image pre-processing using Entropy-function and Moth-Flame (MF) Algorithm, (iii) deep-features extraction using a chosen DL-model, (iv) feature optimization using MF algorithm and serial features concatenation, and (iv) classification and performance confirmation using five-fold cross-validation. …”
Get full text

IVIM-DWI-based radiomic model for preoperative prediction of hepatocellular carcinoma differentiation by ZHUANG Yuxiang, LI Xiaofeng, ZHOU Daiquan

“…Univariate analysis was used to assess the clinical indicators related to HCC differentiation, and then a clinical model was constructed. Pyramidimics software was used to extract the radiomic features of IVIM-DWI functional images, and minimum absolute contraction and selection operator logistic regression algorithm were employed to screen those highly correlated indicators with HCC differentiation. …”
Get full text

Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. …”
Get full text

Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach by Chandra Wadde, Gayatri Routhu, Mark Clemente-Arenas, Surya Prakash Gummadi, Rupesh Kumar

“…The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. …”
Get full text

Large language models in the management of chronic ocular diseases: a scoping review by Jiatong Zhang, Xiaoxi Song, Bocheng Tian, Mingke Tian, Zhichang Zhang, Jing Wang, Ting Fan

“…Future directions emphasize the need for specialized model training, multimodal algorithm optimization, the establishment of a multinational multicenter clinical validation platform, and the construction of an ethical framework for dynamic regulation. …”
Get full text

Development and validation of a carotid plaque risk prediction model for coal miners by Yi-Chun Li, Yi-Chun Li, Yi-Chun Li, Tie-Ru Zhang, Tie-Ru Zhang, Tie-Ru Zhang, Fan Zhang, Fan Zhang, Fan Zhang, Chao-Qun Cui, Chao-Qun Cui, Chao-Qun Cui, Yu-Tong Yang, Yu-Tong Yang, Yu-Tong Yang, Jian-Guang Hao, Jian-Ru Wang, Jiao Wu, Hai-Wang Gao, Ying-Bo Liu, Ming-Zhong Luo, Li-Jian Lei, Li-Jian Lei, Li-Jian Lei

“…The features were initially screened using extreme gradient boosting (XGBoost), random forest, and LASSO regression, and the model was subsequently constructed using logistic regression. …”
Get full text

Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review by Maxime Sasseville, Steven Ouellet, Caroline Rhéaume, Malek Sahlia, Vincent Couture, Philippe Després, Jean-Sébastien Paquette, David Darmon, Frédéric Bergeron, Marie-Pierre Gagnon

“…However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations. ConclusionsThe results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm’s preprocessing stage. …”
Get full text

Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning by FENG Xin, LIU Yanju, TONG Hongfu, QIAN Shuni

“…Combining with the random forest algorithm, this paper constructs a new meteorological drought forecasting model through regression analysis to screen global SST areas of forecasting significance and takes the Pearl River Basin as an example for application tests. …”
Get full text

Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Three widely applicable machine learning algorithms (Random Forests, Gradient Boosting Machine, and XGBoost) were used to screen the metrics, and then the corresponding predictive models were constructed using discriminative analysis, and the best performing model was selected as the target model. …”
Get full text

Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer by Lei Shen, Kang Hou, Jifeng Zhang, Xiaodong Li

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”
Get full text

Prognosis model of patients with breast cancer based on metabolism-related LncRNAs by Dan Zhang, Shiwei Ma, Liling Yang, Hongyuan Liu, Han Jiang, Yan Wang

“…Finally, based on the analysis of the CIBERSORT algorithm, lncRNAs used in the construction of the model had a strong correlation with CD8+T cells, activated CD4+T cells and the polarization of M2 macrophages. …”
Get full text

Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

“…Objectives: The aim of this study was to compare the performance of 4 algorithms for ATTR-CM detection in a heart failure population and assess the risk for harms due to model bias. …”
Get full text