Artificial intelligence in clinical decision support and the prediction of adverse events by S. P. Oei, T. H. G. F. Bakkes, M. Mischi, R. A. Bouwman, R. A. Bouwman, R. J. G. van Sloun, S. Turco

“…Biases in data acquisition, such as population shifts and data scarcity, threaten the generalizability of AI-based CDS algorithms across different healthcare centers. Techniques like resampling and data augmentation are crucial for addressing biases, along with external validation to mitigate population bias. …”
Get full text

FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…The proposed FUSCANet model is evaluated on four different datasets (the public PAD-UFES-20, HAM10000, and ISIC 2019 datasets, and a private dataset) containing images of various skin diseases. …”
Get full text

Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches by Van-Ha Hoang, Jong Weon Lee, Md. Jalil Piran, Chun-Su Park

“…Although there have been multiple surveys on fall detection, most of them focus on assessing fall detection systems using different kinds of sensors, and a comprehensive evaluation of skeleton-based fall detection in RGB videos is lacking. …”
Get full text

UNS Geo: LiDAR Dataset for point cloud classification in urban areas by M. Govedarica, G. Jakovljevic, I. Ruskoviski, V. Pajic

“…Although in the last few years, different methodologies and algorithms have been proposed, precise and detailed point cloud labelling is still challenging. …”
Get full text

Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes by Shoupeng Ding, Chunxiao Huang, Jinghua Gao, Chun Bi, Yuyang Zhou, Zihan Cai

“…We identified T-cell-associated metabolic differentially expressed genes (TCM–DEGs) through integrated differential expression analysis and machine learning algorithms (XGBoost, SVM–RFE, and Boruta). …”
Get full text

DEEP LEARNING-BASED SUPER-RESOLUTION TECHNIQUES: A COMPARATIVE ANALYSIS WITH RECENT INSIGHTS by Renuka Sambhaji Sindge, Maitreyee Dutta, Jagriti Saini

“…Furthermore, this paper presents future directions for future researchers to follow SR trends with potential DL-based algorithms.…”
Get full text

A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
Get full text

Reproduction of reservoir pressure in oil field development: prospects and problems of using methods machine learning by I. N. Ponomarevа, D. A. Martyushev

“…Three oil deposits of one field with different geological and physical conditions were selected as the object of study. …”
Get full text

Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review by Beyza Demirtaş Yılmaz

“…Study design, machine learning algorithms, and audiological measurements were evaluated in the data analysis. …”
Get full text

Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma by Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Fan Yang, Fan Yang, Fan Yang, Fan Yang, Wenjing Hou, Wenjing Hou, Wenjing Hou, Wenjing Hou, Ying Wang, Ying Wang, Ying Wang, Ying Wang, Jiali Mu, Jiali Mu, Jiali Mu, Jiali Mu, Hailing Wang, Hailing Wang, Hailing Wang, Hailing Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei

Get full text

The multi-omics analysis identifies a novel endoplasmic reticulum stress and immune related genes signature in lung adenocarcinoma by Danhe Huang, Yuying Liu, Mingyu Yuan, Xiongwei Wang, Lianqing Hong

“…Tumor microenvironment characteristics were evaluated using the CIBERSORT and ESTIMATE algorithm. …”
Get full text

Study on consistency between liver fat fraction quantification based on photon-counting CT and MRI proton density fat fraction by CAI Xinxin, DENG Rong, XU Xinxin, XU Zhihan, CHANG Rui, DONG Haipeng, LIN Huimin, YAN Fuhua

“…The performance of the adjusted threshold in measuring liver fat content was evaluated in the validation cohort, as well as the consistency across subgroups with different scanning protocols. …”
Get full text

A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning by Bo Zhang, Yumei Qin, Liandi Jiu, Chunming Qin, Jiangbo Wang, Haiqing Zhao

“…Feature analysis and data mining were conducted, and eight different machine learning algorithms were used to build the prediction model. …”
Get full text

CLINICAL AND LABORATORY ASPECTS OF DETECTING SPECIFIC IgE ANTIBODIES TO COW’S MILK AND ITS COMPONENTS by N. A. Alkhutova, N. A. Kovyazina, O. L. Zhizhina

“…An immunochemoluminescent assay of specific IgE antibody levels to the cow milk using IMMULITE 2000/XPi analyzer has revealed its good informative value at different approaches to prediction and evaluation of food allergy treatment, both oriented for a critical cutoff value of 3 МU/L, and by monitoring a decrease in antibody levels. …”
Get full text

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy by Bin Jia, Ziying Mai, Chaoqun Xiang, Qiwen Chen, Min Cheng, Longkai Zhang, Xue Xiao

“…The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. …”
Get full text

Ethical and social issues in prediction of risk of severe mental illness: a scoping review and thematic analysis by Ivars Neiders, Signe Mežinska, Neeltje E. M. van Haren

“…In several cases there is no empirical knowledge that determines whether particular concerns are justified (stigmatisation, use of machine learning algorithms).…”
Get full text

Supercontinuum Generation at 1310nm in a Highly Nonlinear Photonic Crystal Fiber with a Minimum Anomalous Group Velocity Dispersion by Ashkan Ghanbari, Ali Sadr, Hadi Tat Hesari

“…ABSTRACT:In present study ,we intend to investigate the evolution of supercontinuum generation (SCG) through triangular photonic crystal fiber (PCF) at 1310nm by using both full-vector multipole method (M.P.M) and novel concrete algorithms: symmetric  split-step fourier (SSF) and  fourth order runge kutta (RK4) which is an accurate method to solve the general  nonlinear schrodinger equation (GNLSE). …”
Get full text

Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study by Salah Bouktif, Akib Mohi Ud Din Khanday, Ali Ouni

“…ConclusionsConsidering the dynamic nature of suicidal behavior posts, we proposed a fused architecture that captures both localized and generalized contextual information that is important for understanding the language patterns and predict the evolution of suicidal ideation over time. According to Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) XAI algorithms, there was a drift in the features during and before COVID-19. …”
Get full text

MRI R2* and quantitative susceptibility mapping in brain tissue with extreme iron overload by Christoph Birkl, Marlene Panzer, Christian Kames, Anna Maria Birkl-Toeglhofer, Alexander Rauscher, Bernhard Glodny, Elke R. Gizewski, Heinz Zoller

“…Our study in patients with aceruloplasminemia revealed that the choice of reference region significantly influences susceptibility values, with variations exceeding algorithm-dependent differences. Key Points R2* and QSM vary across algorithms in brain tissue with iron overload. …”
Get full text

Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Performance was evaluated using accuracy, precision, recall, and F1-score metrics. …”
Get full text