Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM by Nicholas Niako, Jesus D. Melgarejo, Gladys E. Maestre, Kristina P. Vatcheva

“…Results All imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. …”
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Peculiarities of application of the resources of the Moscow Electronic School platform for the formation of language competence of the students of non-linguistic specialties by Tatyana V. Masharova, Ekaterina K. Starkova, Ivan V. Shunin, Anna S. Pastukhova

“…The peculiarities of the formation of linguistic competence of students of non-linguistic specialties by means of MES include the following: the development of competence proceeds through the search and resolution of problematic situations supported by texts on special topics and posted on the platform; foreign language activities are aimed at solving tasks designed to assimilate the content of linguistic competence at algorithmic and heuristic levels; for the management of educational and speech skills of self-monitoring and self-assessment activities sets of tasks and tests that require systematic work with the resources of the database of materials are used.…”
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Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis by Liu T, Gao L, Li X

“…Three machine learning algorithms were applied to refine the intersected genes. …”
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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…Furthermore, multiple machine learning (ML) algorithms, including both traditional and EL models, are employed to develop optimized predictive models for the flexural ultimate capacity of reinforced UHPC specimens derived from the established database. …”
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Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication by Cristian Daniel Marineci, Andrei Valeanu, Cornel Chiriță, Simona Negreș, Claudiu Stoicescu, Valentin Chioncel

“…The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. …”
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Resectograms: Planning liver surgery with real-time occlusion-free visualization of virtual resections by Ruoyan Meng, Davit Aghayan, Egidijus Pelanis, Bjørn Edwin, Faouzi Alaya Cheikh, Rafael Palomar

“…We enhanced the visualization through improved 3D-to-2D orientation mapping and distortion-minimizing parameterization algorithms. This research contributes to advancing liver surgery planning tools by offering a more accessible and informative visualization method. …”
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Meningococcal Vaccination in High-Risk Patients: A Systematic Approach to Evaluating Coverage and Patient Catch-Up Through Healthcare Databases by Rafael Ruiz-Montero, Álvaro Serrano-Ortiz, Mario Rivera-Izquierdo, Piedad Galvache Murillo-Rico, Ana Moñiz-Díez, María Ángeles Onieva-García, Eloy Girela-López, Inmaculada Salcedo-Leal

“…<b>Conclusions</b>: The identification of high-risk patients through databases using R-coded algorithms is both feasible and effective for identifying and catching-up patients for vaccination. …”
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Object-Specific Multiview Classification Through View-Compatible Feature Fusion by Javier Perez Soler, Jose-Luis Guardiola, Nicolás García Sastre, Pau Garrigues Carbó, Miguel Sanchis Hernández, Juan-Carlos Perez-Cortes

“…Through experimental evaluations, we demonstrate that the proposed VCFF method outperforms state-of-the-art MVC algorithms, especially in open-set scenarios, where the set of possible objects is not fully known in advance. …”
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Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis by Yan Wu, Jianhong Wang, Jianhong Wang, Bo Chen, Yuxue Guo, Yuxue Guo, Ping Gan, Yanbing Han

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”
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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. …”
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Evaluation of internal reference genes for photodynamic inactivation-based quantitative PCR studies in Staphylococcus aureus by Kamila Koči, Jozefína Theissová, Katarína Bilská, Juraj Koči

“…Raw qPCR data of six candidate reference genes (femA, glyA, hu, rho, rpoD, rrsC) were analyzed with four standard algorithms, geNorm and NormFinder included in a GenEx software package, as well as with BestKeeper and ΔCq to determine the stability of the genes. …”
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Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients by Indika Rajakaruna, Mohammad Hossein Amirhosseini, Mike Makris, Mike Laffan, Yang Li, Deepa J. Arachchillage

“…Methods: We used feature ranking through recursive feature elimination with AI algorithms (logistic regression and random forest classifier) and standard statistical methods to identify the significant factors that contribute to developing VTE in COVID-19 patients using a large dataset from “Coagulopathy associated with COVID-19,” a multicenter observational study. …”
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Incidence and prevalence of idiopathic pulmonary fibrosis: a systematic literature review and meta-analysis by Negar Golchin, Aditya Patel, Julia Scheuring, Victoria Wan, Kimberly Hofer, Jean-Paul Collet, Brandon Elpers, Tamara Lesperance

“…Additional contributing factors include variations in case identification algorithms, differences in diagnostic definitions and regional differences in occupational and environmental exposures. …”
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…Additionally, we evaluated the performance of the nine machine learning algorithms to determine the optimal model. The Shapley additive explanation (SHAP) method was subsequently employed to prioritize factor importance and refine the final model. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis. …”
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A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort by Yun C. Lin, Daniel Mallia, Andrea O. Clark-Sevilla, Adam Catto, Alisa Leshchenko, Qi Yan, David M. Haas, Ronald Wapner, Itsik Pe’er, Anita Raja, Ansaf Salleb-Aouissi

“…However, since our model includes various factors that exhibit a positive correlation with PLGF, such as blood pressure measurements and BMI, we have employed an algorithmic approach to disentangle this bias from the model. …”
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Limitations of using artificial intelligence services to analyze chest x-ray imaging by Yuriy A. Vasilev, Anton V. Vladzymyrskyy, Kirill M. Arzamasov, Igor M. Shulkin, Elena V. Astapenko, Lev D. Pestrenin

“…However, when interpreting X-ray scans using artificial intelligence, radiologists still experience several routine restrictions that should be considered in issuing a medical report and require the attention of artificial intelligence developers to further improve the algorithms and increase their efficiency. AIM: To identify restrictions of artificial intelligence services for analyzing chest X-ray images and assesses the clinical significance of these restrictions. …”
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The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…As data become increasingly large, diverse and fast-moving, conventional processing systems often fall short of the performance required for modern analytics.Objective: This research seeks to thoroughly assess the performance of two prominent big data processing frameworks-Apache Spark (in-memory computing) and MapReduce (disk-based computing)-with a focus on applying random forest algorithms to predict stock prices. The primary objective is to assess and compare their effectiveness in handling large-scale financial datasets, focusing on key aspects such as predictive accuracy, processing speed and scalability.Methods: The investigation uses the MapReduce methodology and Apache Spark independently to analyse a substantial stock price dataset and to train a random forest regressor. …”
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