Knowledge, Readiness, Willingness-to-Use, and Willingness-to-Pay for Telehealth in Nonlife-Threatening Emergency Department Visits by Vahé Heboyan, Phillip Coule, Davide Mariotti, Gianluca De Leo

“…We did not observe any statistically significant differences in willingness-to-use. However, we observed statistically significant differences in the willingness-to-pay $50 by gender (p < 0.01), by currently having a regular doctor/clinic (p < 0.05), and by health insurance status. …”
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THE MAIN CAUSES OF UNSATISFACTORY OUTCOMES OF TREATMENT FOR FOOT INJURIES by V. O. Kalensky, P. A. Ivanov

“…It is advisable to continue research to find the best algorithm for treatment in these cases.…”
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Potential Metabolic Markers in the Tongue Coating of Chronic Gastritis Patients for Distinguishing Between Cold Dampness Pattern and Damp Heat Pattern in Traditional Chinese Medici... by Yuan S, Zhang R, Zhu Z, Zhou X, Zhang H, Li X, Hao Y

“…The model quality was evaluated by 7-fold cross-validation, and the model validity was evaluated based on R²Y (interpretability of categorical variable Y) and Q² (predictability of the model), and the permutation test was used for further verification. …”
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Comment on “Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation” by Hinpetch Daungsupawong, Viroj Wiwanitkit

“…Additionally, a more thorough exploration of the current limitations in diagnosing these tumors would have provided a more comprehensive understanding of the issue.Moving forward, future research should focus on developing AI algorithms that can accurately differentiate between different types of odontogenic tumors based on their unique characteristics. …”
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Clinical significance of a machine learning model based on short-term changes in NT-proBNP after TAVR by Yu Mao, Mengen Zhai, Ping Jin, Gejun Zhang, Haibo Zhang, Lai Wei, Xiaoke Shang, Jian Liu, Yingqiang Guo, Xiangbin Pan, Yang Liu, Jian Yang

“…Methods: The differences in the NT-proBNP ratio between baseline, 30-day, and 6-month follow-up of patients in the internal derivation cohort (n = 1115) were recorded as D1 and D2; the difference ratio of the NT-proBNP ratio (D2/D1) was recorded as DR. …”
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Assessing uncertainties in parton showers at double logarithmic accuracy for jet quenching studies by Carlota Andres, Liliana Apolinário, Néstor Armesto, André Cordeiro, Fabio Dominguez, José Guilherme Milhano

“…To probe the impact of these differences, we introduce a simplified model for in-medium energy loss based on formation time and colour decoherence, enabling us to evaluate the sensitivity of quenching observables to the underlying space-time structure of the vacuum shower. …”
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Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
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Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction by Omar Abdullatif Jassim, Mohammed Jawad Abed, Zenah Hadi Saied Saied

“…Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. …”
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Interpretability-Oriented Adjustment of K-Means: A Multiple-Objective Particle Swarm Optimization Framework by Liang Chen, Leming Sun, Caiming Zhong

“…Clustering is an unsupervised machine learning technique used to partition unlabeled data into different groups. However, traditional clustering methods only provide a set of results without any explanations. …”
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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

“…Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. …”
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Providing a Robust Dynamic Pricing Model and Comparing It with Static Pricing in Multi-level Supply Chains Using a Game Theory Approach by Sara Mehrjoo, Hanan Amoozad Mahdirji, Jalil Heidary Dahoei, Seyyed Hossein Razavi Haji Agha, Mahnaz Hosseinzadeh

“…For data analysis, a genetic algorithm, particle accumulation optimization, and MATLAB software were employed. …”
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NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…In this study, using the MS UAV NDVI map as reference, a comprehensive evaluation approach was applied where each pixel of the NDVI prediction maps produced by categorical boosting (CatBoost), light gradient boosting machine (LightGBM) and a stacking ensemble learning model obtained from the combination of both algorithms, whose performance in NDVI estimation has not been tested extensively before. …”
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Stain Normalization of Histopathological Images Based on Deep Learning: A Review by Chuanyun Xu, Yisha Sun, Yang Zhang, Tianqi Liu, Xiao Wang, Die Hu, Shuaiye Huang, Junjie Li, Fanghong Zhang, Gang Li

“…However, color variations caused by differences in tissue preparation and scanning devices can lead to data distribution discrepancies, adversely affecting the performance of downstream algorithms in tasks like classification, segmentation, and detection. …”
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Genomic Analysis of Reproductive Trait Divergence in Duroc and Yorkshire Pigs: A Comparison of Mixed Models and Selective Sweep Detection by Changyi Chen, Yu He, Juan Ke, Xiaoran Zhang, Junwen Fei, Boxing Sun, Hao Sun, Chunyan Bai

“…Additive and dominant genetic effects were partitioned and evaluated by using the combination of the linear mixed models (LMM) and ADDO’s algorithm (LMM + ADDO). …”
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Intensifying cropping sequences in the US Central Great Plains: an in silico analysis of a sorghum–wheat sequence by Lucia Marziotte, Ana J. P. Carcedo, Daniel Rodriguez, Laura Mayor, P. V. Vara Prasad, Ignacio A. Ciampitti, Ignacio A. Ciampitti

“…Using terciles of historical input costs for all crop sequences we calculated three cost scenarios low, intermediate, and high. A fuzzy-C means algorithm was used to classify regions based on crop sequences’ profits, resulting in four clusters.Results and discussionResults included two regions where sorghum-wheat was more profitable than the monocrops i.e., one with lower profits (S+W lower), and a second one with higher profits (S+W higher); a third cluster where wheat monocrop was most profitable (W), and lastly one cluster showing no difference between the sorghum-wheat sequence and the wheat monocrop (S+W or W). …”
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Imbalance between skeletal muscle and intermuscular fat predicts treatment failure in Crohn’s disease: an imaging biomarker for risk stratification by Ziman Xiong, Yufan Wang, Yuchen Jiang, Yaqi Shen, Zhen Li

“…Cox proportional hazards analysis identified predictors of escalation; mediation analysis evaluated inflammatory-nutritional pathways.Results Among 157 patients (penetrating: n = 42; non-penetrating: n = 115), treatment escalation rates were 64.3% (27/42) and 53.0% (61/115) respectively, without significant intergroup difference (p = 0.21). …”
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Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women by Chen A, Chang X, Bian X, Zhang F, Ma S, Chen X

“…In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. …”
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CO21 | Viscoelastic testing in inherited bleeding disorders: a cross-sectional comparison between viscoelastic coagulation monitoring (VCM) and rotational thromboelastometry (ROTE...

“…Spearman correlation (ρ) was used: (i) to assess the association between residual FVIII and VCM/ROTEM parameters in HA; (ii) to evaluate agreement between homologous VCM and ROTEM parameters in the entire cohort. …”
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Identification and experimental validation of ulcerative colitis-associated hub genes through integrated WGCNA and lysosomal autophagy analysis by Yuanpei Zhao, Yijun Li, Qingwen Xu, Lili Ding, Weiming Li, Qinghua Zou, Yichen Hu, Kaiwen Shi, Hongyuan Liu

“…Immune cell infiltration of these gene sets was evaluated using the CIBERSORT algorithm. Lysophagy-related genes set were retrieved from the GeneCards database. …”
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Impact of anthropogenic disturbance and climate on bamboo distribution in shifting cultivation landscapes of Northeast India by Muna Tamang, Subrata Nandy, Ritika Srinet, Yamini Bhat, Hitendra Padalia, Arun Jyoti Nath, Ashesh Kumar Das, R. P. Singh

“…The influence of climatic drivers on bamboo distribution was analyzed using the RF algorithm, and vapour pressure deficit was identified as the most influential factor. …”
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