Gender and age characteristics and atrial fibrillation during long-term telemonitoring of the ECG by V.M. Bogomaz, I.O. Berdnyk, L.I. Lysa

“…The primary analysis of the records was carried out using Cardio.ai artificial intelligence algorithms, re-decryption and validation by certified doctors. …”
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Appropriateness of NHS 111 Wales outcomes—using the Call Prioritisation Streaming System: a RAND/UCLA modified Delphi method by Craig Brown, Conrad Fivaz, Rebecca Malin, Mike Brady, Rhiannon Elizabeth Roynon, Peter Noblett

“…It achieves this by integrating individual patient data with a comprehensive computerised knowledge base, employing advanced software algorithms to produce recommendations and dispositions.Objectives While CPSS offers many advantages, its introduction marked a major shift in clinical digital processes. …”
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Cropping and Transformation Features of Non-Grain Cropland in Mainland China and Policy Implications by Yizhu Liu, Ge Shen, Tingting He

“…Remote sensing is regarded as the most effective and accurate method for this purpose, but existing studies have mainly focused on algorithms operating at the local scale or exploring grain-producing capability from the perspective of agricultural space. …”
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Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

“…These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). …”
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Identification of neoantigen epitopes in cervical cancer by multi-omics analysis by Jing Yuan, Na Xu, Xueqi Gong, Jihui Ai, Kezhen Li, Yingyan Han

“…Focusing on MHC class I epitopes recognized by CD8 + T cells, we predicted potential neoantigen peptides using the NetMHCpan-4.0 and NetCTL-1.2 algorithms. To further confirm the immunogenicity of the synthesized peptides, we performed flow cytometry and real-time PCR in vivo to examine markers of T cell activation and cytotoxicity. …”
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Artificial Intelligence (AI) in Neurofeedback Therapy Using Electroencephalography (EEG), Heart Rate Variability (HRV), and Galvanic Skin Response (GSR): A Review by Teddy Marcus Zakaria, Armein Zainal R. Langi, Muhamad Sophian Nazaruddin, Isa Anshori

“…These directions include the development of robust AI algorithms, the integration of NFT with emerging technologies such as virtual reality, ethical and societal implications, and AI-powered NFT applications. …”
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MORPHOLOGICAL VARIABILITY OF LEAVES OF ACER NEGUNDO L. POPULATIONS IN THE ALTITUDINAL GRADIENT OF THE NORTH-WEST CAUCASUS (REPUBLIC OF ADYGEA) by Evgenia M. Ednich, Irina V. Chernyavskaya, Tatyana N. Tolstikova, Mariet N. Khagur, Marat V. Aliev

“…Morphological features of the leaf (maximum length of the leaf blade, maximum width, angle between the central and lateral vein of the upper leaf) were measured with a caliper with an accuracy of ± 0.01 mm and a protractor with an accuracy of 1°; leaf thickness – by a Turgoromer-1 device with a measuring head of the TN10-60T system; leaf area calculation – using algorithms in Python 3.1. Statistical analysis of the morphological features variability was performed by means of descriptive statistics methods, principal component analysis, correlation analysis, variance and cluster analysis and linear regression with the programming language Python 3.11.1. …”
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One-Node One-Edge Dimension-Balanced Hamiltonian Problem on Toroidal Mesh Graph by Yancy Yu-Chen Chang, Justie Su-Tzu Juan

“…The DBC problem on toroidal mesh graph <i>T_m</i>_,<i>n</i> is appropriate for designing simple algorithms with low communication costs and avoiding congestion. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. …”
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KOC_Net: Impact of the Synthetic Minority Over-Sampling Technique with Deep Learning Models for Classification of Knee Osteoarthritis Using Kellgren–Lawrence X-Ray Grade by Syeda Nida Hassan, Mudassir Khalil, Humayun Salahuddin, Rizwan Ali Naqvi, Daesik Jeong, Seung-Won Lee

“…KOA identification using deep learning (DL) algorithms has gained popularity during the past few years. …”
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Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study by Salah Bouktif, Akib Mohi Ud Din Khanday, Ali Ouni

“…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. …”
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Practical applications of methods to incorporate patient preferences into medical decision models: a scoping review by Jakub Fusiak, Kousha Sarpari, Inger Ma, Ulrich Mansmann, Verena S. Hoffmann

“…Abstract Background Algorithms and models increasingly support clinical and shared decision-making. …”
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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain by Jay Gopal, Jonathan Bao, Tessa Harland, Julie G. Pilitsis, Steven Paniccioli, Rachael Grey, Michael Briotte, Kevin McCarthy, Ilknur Telkes

“…Our findings suggest that combination of subjective self-reports, intraoperatively obtained EEGs, and well-designed machine learning algorithms might be potentially used to distinguish responders and nonresponders. …”
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A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. …”
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Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients by Mohit D. Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, M.P. Girish, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf

“…Patients were divided into two groups: training (n = 1360) and validation dataset (n = 340). Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. …”
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Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy by Jing Wang, Jingjing Dai, Na Li, Chulong Zhang, Jiankai Zhang, Zuledesi Silayi, Haodi Wu, Yaoqing Xie, Xiaokun Liang, Huailing Zhang

“…We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD₉₅ values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. …”
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Molecular characterization and prognostic modeling associated with M2-like tumor-associated macrophages in breast cancer: revealing the immunosuppressive role of DLG3 by Ziqiang Wang, Jing Zhang, Huili Chen, Xinyu Zhang, Kai Zhang, Feiyue Zhang, Yiluo Xie, Hongyu Ma, Linfeng Pan, Qiang Zhang, Min Lu, Hongtao Wang, Chaoqun Lian

“…Consensus clustering analysis identified three molecular subtypes with distinct clinical features, and we explored potential differences in genomic mutations, pathway enrichment, and immune infiltration in patients between subtypes. Machine learning algorithms were used to screen key genes and construct M2-like macrophage-associated prognostic models. …”
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Renal parenchymal volume analysis: Clinical and research applications by Carlos Munoz‐Lopez, Kieran Lewis, Nityam Rathi, Eran Maina, Akira Kazama, Anne Wong, Angelica Bartholomew, Worapat Attawettayanon, Yunlin Ye, Zhiling Zhang, Wen Dong, Rebecca A. Campbell, Nicholas Heller, Erick Remer, Christopher Weight, Steven C. Campbell

“…PVA provides improved accuracy when compared to NRS‐based estimates or non‐SRF‐based algorithms. PVA can be performed in minutes using routine preoperative cross‐sectional imaging and can be readily applied at the point of care. …”
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AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis by Hind M. AlOsaimi, Aseel M. Alshilash, Layan K. Al-Saif, Jannat M. Bosbait, Roaa S. Albeladi, Dalal R. Almutairi, Alwaleed A. Alhazzaa, Tariq A. Alluqmani, Saud M. Al Qahtani, Sara A. Almohammadi, Razan A. Alamri, Abdullah A. Alkurdi, Waleed K. Aljohani, Raghad H. Alraddadi, Mohammed K. Alshammari

“…AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status. …”
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Treatment Strategies and Prognostic Outcomes in Acute Limb Ischemia: A Systematic Review and Meta-Analysis Comparing Thrombolytic Therapy and Open Surgical Interventions by Mircea Cătălin Coșarcă, Nicolae Alexandru Lazăr, Suzana Vasilica Șincaru, Bogdan Corneliu Bandici, Eduard Costin Argatu, Cosmin Carașca, Ráduly Gergő, Dorin Constantin Dorobanțu, Cristian Trâmbițaș, Adrian Vasile Mureșan

“…Future research should focus on refining treatment algorithms, investigating novel thrombolytic agents, and expanding the role of minimally invasive techniques to improve long-term outcomes while mitigating complications such as bleeding and reperfusion injuries.…”
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