Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…YuHan Dong,1 Jihong Hu,2 Xuerou Meng,2 Bin Yang,3 Chao Peng,1 Wei Zhao1 1Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China; 2Department of Interventional Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China; 3Medical Imaging Center, The First Hospital of Kunming, Kunming, 650051, People’s Republic of ChinaCorrespondence: Wei Zhao, Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China, Email kyyyzhaowei@foxmail.com Chao Peng, Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China, Email 609101429@qq.comPurpose: This study constructs a predictive model for hepatocellular carcinoma (HCC) transarterial chemoembolization (TACE) refractoriness using a machine learning (ML) algorithm and verifies the predictive performance of different algorithms.Patients and Methods: Clinical and magnetic resonance imaging (MRI) data of 131 patients (48 with TACE refractoriness) who underwent repeated TACE treatment for HCC were retrospectively collected. …”
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In-vitro diagnostic point-of-care tests in paediatric ambulatory care: A systematic review and meta-analysis. by Oliver Van Hecke, Meriel Raymond, Joseph J Lee, Philip Turner, Clare R Goyder, Jan Y Verbakel, Ann Van den Bruel, Gail Hayward

“…Data relating to at least one outcome were available for 89,439 children of whom 45,283 had a POCT across six conditions or infection syndromes: malaria (n = 14); non-specific acute fever 'illness' (n = 7); sore throat (n = 5); acute respiratory tract infections (n = 5); HIV (n = 3); and diabetes (n = 1). Outcomes centred around decision-making such as prescription of medications or hospital referral. …”
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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

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Impact of AI and Machine Learning on Financial Services by Patil Smita, Mailcontractor Rahul

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Research status and progress on key technologies of intelligent orchard by CUI Hongwei, LU Xiaoxuan, YANG Yaqing, LU Xinyi, MA Hao, JI Jiangtao, JIN Xin, LI Xiuzhen, ZHAO Zimeng, ZENG Ningning

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Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…This study not only verifies the effectiveness of combining multisource data and advanced algorithms but also provides a scientific basis for the precision management and decision-making of rice cultivation.…”
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Whose Bias is it, Anyway? The Need for a Four-Eyes Principle in AI-Driven Competion Law Proceedings by Jerome De Cooman

“…Cognitive biases. – II.2. Noise. – II.3. Bias and noise in competition law procedure. – III. …”
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Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population. by Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Nurulain Ibrahim, Xue Ning Kiew, Nafiza Mat Nasir, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff

“…Ensemble model were also created using three commonly used meta learners, including RF, Generalized Linear Model (GLM), and Gradient Boosting Model (GBM). …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…Through integrated bioinformatics approaches, including differential expression analysis, univariate Cox regression, and three machine learning algorithms (Boruta, GBM, and RFS), we identified prognosis-related MMCG. …”
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Does metaverse improve recommendations quality and customer trust? A user-centric evaluation framework based on the cognitive-affective-behavioural theory by Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Osama Halabi, Raian Ali

“…Recommendation agents (RAs) have proven to be effective decision-making tools for customers, as they can boost trust and loyalty when customers shop online. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…Basophil count, while ranked highest by SHAP, showed low sensitivity, highlighting the difference between algorithmic weight and bedside utility. Conclusion: These findings support the integration of routine, readily available laboratory data into an explainable AI framework to accurately predict culture positivity. …”
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Oil Development Engineering Company, Tehran, Iran by Seyed Ali Mohammad Tajalli, Mazda Moattari, Vahid Naghavi, Mohammad Reza Salehizadeh

“…Furthermore, the operational procedure of this framework involves three key stages. In the “Data Collection” phase, sensor data are monitored by observing nodes. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…<h4>Results</h4>The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. …”
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Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors by Fan Yang, Fan Yang, Fan Yang, Fan Yang, Chun-wei Liu, Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei, Mo Yang, Mo Yang, Mo Yang

“…The patient dataset was randomly divided into a training set and a validation set at a ratio of 7:3. Leveraging the XGBoost (XGB) algorithm within the Scikit-learn (Sklearn) machine-learning library, three distinct predictive models were developed: a clinical ultrasound imaging model (US model), an ultrasonographic radiomics model (US radiomics model), and a combined model integrating both clinical, ultrasound, and radiomics features. …”
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Identification of glycolysis-related molecular subtypes and prognostic model in intrahepatic cholangiocarcinoma by Yue Wang, Pengxiang Wang, Hui Liu, Haokang Feng, Muzi Cao, Zefan Zhang, Keqiang Rao, Jia Fan, Xiutao Fu, Yunfan Sun

“…A 9-gene risk model (ALDH1B1, DDIT4, GALE, HK1, HMMR, PGAM1, PGK1, PLOD1, SAP30) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and multivariate Cox regression analysis. …”
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Building Energy Optimization Using an Improved Exponential Distribution Optimizer Based on Golden Sine Strategy Minimizing Energy Consumption Under Uncertainty by Mohammad Ali Karbasforoushha, Mohammad Khajehzadeh, Suraparb Keawsawasvong, Lapyote Prasittisopin, Thira Jearsiripongkul

“…The superiority of the IEDO is confirmed by comparing its performance with several well-known algorithms and previous studies in deterministic cases. …”
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Early Recognition of Secondary Asthma Caused by Lower Respiratory Tract Infection in Children Based on Multi-Omics Signature: A Retrospective Cohort Study by Rao Z, Zhang S, Xu W, Huang P, Xiao X, Hu X

“…The AUC, sensitivity, and specificity of nomogram prediction for secondary asthma in children with LTRIs were 0.817(95CI: 0.760– 0.874), 82.3%, and 76.6%, respectively; The AUC of decision tree prediction for secondary asthma in children with LTRIs is 0.926(95% CI: 0.869– 0.983), with a sensitivity of 96.7% and a specificity of 87.8%.Conclusion: LTRIs in children are mainly caused by Staphylococcus aureus, Streptococcus pneumoniae, Staphylococcus epidermidis, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa; In addition, machine learning combined with multi-omics prediction models has shown good ability in predicting LTRIs combined with asthma, providing a non-invasive and effective method for clinical decision-making.Keywords: children, lower respiratory tract infection, pathogenic bacteria, radiomics, untargeted metabolomics, asthma, prediction model…”
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Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study by Ke Z, Shen L, Shao J

“…The logical model included shear wave elastography (SWE) related to maximum, minimum, centre, ratio 1, pathomics (Feature 1, Feature 3, and Feature 5) and a nomogram of the GLRM was drawn. …”
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COVID-19 Pandemic Course 2020–2022: Description by Methods of Mathematical Statistics and Discrete Mathematical Analysis by Gvishiani Alexei, Odintsova Anastasiya, Rovenskaya Elena, Boyarshinov Grigory, Belov Ivan, Dobrovolsky Michael

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