The value of MRI-based radiomics and clinicoradiological data for the detection of forkhead box protein A1 gene mutated prostate cancer by Lin Deng, Ruchuan Chen, Bingni Zhou, Guoqing Hu, Hualei Gan, Ling Zhang, Liangping Zhou, Kefu Liu, Xiaohang Liu

“…Abstract This study aimed to develop models for predicting forkhead box protein A1 (FOXA1) gene mutations in prostate cancer using clinicoradiological and MRI radiomics data. Totally 367 prostate cancer patients (109 with FOXA1 mutations and 258 without) from three centers underwent multiparametric MRI. …”
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Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample by Olivia Metcalf, Karen E. Lamb, David Forbes, Meaghan L. O’Donnell, Tianchen Qian, Tracey Varker, Sean Cowlishaw, Sophie Zaloumis

“…Five summary measures from stress scores collected 10 min prior to each EMA were selected using factor analysis of 24 candidates.Results: A high area under the receiver operating curve (AUC) was found for a logistic mixed effects model including these measures as predictors, ranging 0.761 (95% CI:0.569–0.921) to 0.899 (95% CI:0.784–0.980) across cross-validation methods.Conclusions: While the predictive performance may be overly optimistic due to the outcome prevalence (13.8%) and requires replication with larger datasets, our promising findings have significant methodological and clinical implications for researchers looking to build novel prediction and treatment approaches to respond to posttraumatic mental health.…”
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Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics DatasetMendeley Data by Yu-Yan Pang, Zhong-Qing Tang, Chang Song, Ning Qu, Jing-Yu Chen, Dan-Dan Xiong, Zhen-Bo Feng, Gang Chen

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Data-driven EV charging infrastructure with uncertainty based on a spatial–temporal flow-driven (STFD) models considering batteries by Talal Alharbi, Ahmed Abdalrahman, Mostafa H. Mostafa

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Red cell distribution width and clinical outcomes in sepsis patients infected with Escherichia coli using data from MIMIC-IV by Kexiang Qin, Yingjie Su, Ning Ding

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Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets by Hannah Patrick, Helen Cole, Kim Keltie, Paola Cognigni, Sam Gross, Samuel Urwin, Julie Burn, Lee Berry, Andrew Sims

“…This study aimed to determine longitudinal patient outcomes by linking to routine datasets: Hospital Episode Statistics (HES) Admitted Patient Care (APC) and Office of National Statistics.Methods Two methods of linkage were compared, using identifiable (NHS number, date of birth, postcode, gender) and non-identifiable data (hospital trust, age in years, admission, discharge and operation dates, operation and diagnosis codes). …”
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A High-Quality Underwater Acoustic Dataset for Algorithm Development and Analysis by Victor Lobo, Nuno Pessanha Santos, Ricardo Moura

“…Abstract As data becomes increasingly available, relying on quality datasets for algorithm analysis and development is essential. …”
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Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study by Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon

“…Model 1 used early hospitalization data (up to 1 day after admission), and Model 2 used all the data. …”
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Association of triglyceride glucose and obesity indices with chronic obstructive pulmonary disease in US adults: data from 2013 to 2018 NHANES by Jinzhan Chen, Ayun Chen, Shuwen Yang, Fuhan Zhang, Hongni Jiang, Jiaxin Liu, Congyi Xie

“…Methods This was a cross-sectional study based on data from the NHANES 2013–2018. Weighted logistic regression (WLR), restricted cubic sample (RCS), and receiver operating characteristic (ROC) curves were leveraged to examine the links of the TyG index and obesity indices with COPD. …”
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Transcatheter mitral valve edge-to-edge repair in patients with severe mitral regurgitation: data from the MitraClip Russia Trial by T. E. Imaev, A. E. Komlev, I. V. Kuchin, M. A. Saidova, A. S. Kolegaev, M. I. Makeev, D. V. Salichkin, P. M. Lepilin, A. A. Margolina, S. A. Boytsov, R. S. Akchurin

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Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering by FuDong Wen, Yue Su, Dan Liu, YuPeng Wang, MeiNa Liu

“…Abstract Background High-dimensional proteomics data present significant challenges in biomarker discovery due to technical noise, feature redundancy, and multicollinearity. …”
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Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning by P. Shane Crawford, Mohammad A. Al-Zarrad, Andrew J. Graettinger, Alexander M. Hainen, Edward Back, Lawrence Powell

“…The Extreme Events Web Viewer (EEWV) presented as part of the methodology is a GIS-based web repository storing spatial and temporal data describing communities before and after disasters and facilitating data analysis techniques. …”
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Key Technologies in Intelligent O&M System Construction for Urban Rail Transit Communication by WANG Xianlei

“…The multi-source heterogeneous data fusion and analysis technologies are applied to communication system fault diagnosis and prediction. …”
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The Impact of Neoadjuvant versus Adjuvant Chemotherapy on Survival Outcomes in Locally Advanced Breast Cancer by Farhad Ghasemi, Muriel Brackstone

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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…Following evaluation via confusion matrix analysis, evaluation metrics, and examination of receiver operating characteristics curve, it emerged that the RF algorithm exhibits superior performance when applied to our multi-resistivity data set. …”
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Combination of ultrasound-based radiomics and deep learning with clinical data to predict response in breast cancer patients treated with neoadjuvant chemotherapy by Wu Tenghui, Liu Xinyi, Si Ziyi, Zhang Yanting, Ma Ziqian, Zhu Yiwen, Gan Ling

“…Multiple machine learning algorithms were employed to model and validate the diagnostic performance of different types of features. Finally, clinical data, radiomics, and deep learning features were integrated to form a fusion model, which was evaluated using receiver operating characteristic (ROC) analysis.ResultsThe combined model achieved the highest predictive performance for pathological complete response (pCR) across both cohorts. …”
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SAVE-T: Safety Analysis Visualization and Evaluation Tool by Yuan Zhu, Sami Demiroluk, Kaan Ozbay, Kun Xie, Hong Yang, Di Sha

“…In this paper, a new web-based data visualization tool called Safety Analysis Visualization and Evaluation Tool (SAVE-T) was introduced. …”
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Real‐Time Coseismic Slip Estimation via the GNSS Carrier Phase to Fault Slip Approach: A Case Study of the 2016 Kumamoto Earthquake by Yusuke Tanaka, Yusaku Ohta, Shin'ichi Miyazaki

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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…For yield estimation, feature selection was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) to increase model efficiency and interpretability. …”
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Assessing hand motor function in chronic immune-mediated neuropathies: a proof-of-concept study using a data glove by Elisa Gilliam, Pascal Achenbach, Gernot J. Suemmermann, Manuel N. Wessely, Peter Rossmanith, Maike F. Dohrn, Jörg B. Schulz, Anne Waschbisch, Robert Brunkhorst

“…We calculated correlation coefficients, performed Receiver Operating Characteristic analysis, as well as correlation analyses for the glove data and clinical outcome parameters. …”
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