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machine learning models in predicting failure of Helicobacter pylori treatment: A two country validation study

HELICOBACTER 2024年第1期29卷 e13051-e13051页

作者： Jiang, Fang Lui, Thomas K. L. Ju, Chengsheng Guo, Chuan-Guo Cheung, Ka Shing Lau, Wallis C. Y. Leung, Wai K. Univ Hong Kong Li Ka Shing Fac Med Sch Clin Med Dept Med Hong Kong Peoples R China UCL Sch Pharm Res Dept Practice & Policy London England Capital Med Univ Beijing Friendship Hosp Dept Gastroenterol Beijing Peoples R China Univ Hong Kong Li Ka Shing Fac Med Sch Clin Med Dept MedPokfulam Hong Kong Peoples R China

Background: The success rate of clarithromycin-containing Helicobacter pylori treatment had declined globally. This study aims to explore the role of different machine learning algorithms in predicting failure of H. pylori treatment. Materials and Methods: We included 84,609 adult patients who had received the first course of clarithromycin-containing triple therapy for H. pylori in Hong Kong from 2003 to 2013 as training set. Results were validated in patients who had received similar triple therapy with 27,736 Hong Kong patients between 2014 and 2017 (internal cohort);and 18,050 UK patients between 2012 and 2017 (external cohort). The performance of 11 available machine learning algorithms were used to predict the failure of triple therapy. The performance was determined by the area under receiver operating characteristic curve (AUC). Results: The treatment failure rates in the training, internal and external validation cohort was 5.9%, 9.5%, and 6.1%, respectively. In the internal validation set, Extra-Tree (ET) Classifier had the best AUC (0.88;95% CI, 0.87-0.88), sensitivity (79.6%;95% CI, 79.0-80.2) and specificity (79.4%;95% CI, 79.0-79.8). In the external validation set, ET Classifier also had the best AUC (0.85;95% CI, 0.85-0.86), sensitivity (80.1%;95% CI, 79.5-80.9), and specificity (80.2%;95% CI, 78.8-81.3). Top features of importance used by ET Classifier in predicting treatment failure included time interval between antibiotic use and triple therapy (48.8%), age (29.1%) and triple therapy regime (6.28%). Conclusions: machine learning algorithm, based on simple baseline clinical parameters, could help to identify patients at high risk of failure from clarithromycin-containing triple therapy for H. pylori.

关键词： artificial intelligence Helicobacter pylori machine learning algorithms triple therapy

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Accelerated First-Order Optimization algorithms for machine learning

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PROCEEDINGS OF THE IEEE 2020年第11期108卷 2067-2082页

作者： Li, Huan Fang, Cong Lin, Zhouchen Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin 300071 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Peking Univ Sch Elect Engn & Comp Sci EECS Key Lab Machine Percept MOE Beijing 100871 Peoples R China

Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts have been put on designing theoretically and practically fast algorithms. This article provides a comprehensive survey on accelerated first-order algorithms with a focus on stochastic algorithms. Specifically, this article starts with reviewing the basic accelerated algorithms on deterministic convex optimization, then concentrates on their extensions to stochastic convex optimization, and at last introduces some recent developments on acceleration for nonconvex optimization.

关键词： Numerical analysis machine learning algorithms Optimization methods machine learning Convergence Complexity theory Approximation algorithms Convex functions Acceleration convex optimization deterministic algorithms machine learning nonconvex optimization stochastic algorithms

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Construction of the miRNA/Pyroptosis-Related Molecular Regulatory Axis in Abdominal Aortic Aneurysm: Evidence From Transcriptome Data Combined With Multiple machine learning Approaches Followed by Experiment Validation

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JOURNAL OF IMMUNOLOGY RESEARCH 2024年第1期2024卷 1429510页

作者： Su, Yongchao Lu, Chuangang Chen, Shuchen Fujian Med Univ Union Hosp Dept Thorac Surg Fuzhou 350001 Fujian Province Peoples R China Third Peoples Hosp Hainan Prov Sanya Cent Hosp Dept Cardiothorac Surg Sanya 572000 Hainan Province Peoples R China

Background: Abdominal aortic aneurysm (AAA) represents a permanent and localized widening of the abdominal aorta, posing a potentially lethal risk of aortic rupture. Several recent studies have highlighted the role of pyroptosis, a pro-inflammatory programed cell death, as critical molecular regulators in AAA occurrence, progression, and rupture. However, the potential effects of pyroptosis in AAA and its upstream microRNA (miRNA) have not been comprehensively clarified. Methods: Through a search of the gene expression omnibus (GEO) database, the expression profiles of mRNAs (GSE7084, GSE57691, and GSE98278) and miRNAs (GSE62179) and corresponding clinical features were downloaded, respectively. Expression profiles of 15 AAA and 10 normal vascular samples were consecutively collected for in vitro experimentation and subsequent analysis. Various machine learning techniques were employed to identify hub pyroptosis-related genes (PRGs), leading to the development of a predictive model termed the PRG classifier. Quantitative real-time-polymerase chain reaction (qRT-PCR), western blot (WB), and enzyme-linked immunosorbent assay (ELISA) were used to confirm the expression of the hub PRGs. The diagnostic and predictive capabilities of the model were comprehensively evaluated in GEO and hospital cohorts. Then, the crucial immune cell infiltration and molecular pathways implicated in the initiation and rupture of AAA and their association with pyroptosis were explored. Lastly, a miRNA/hub pyroptosis-related molecular regulatory axis was constructed using the TargetScan dataset, which was further explored through loss-of-function assays. Results: Differential analysis, enrichment score analysis, and principal component analysis (PCA) revealed that pyroptosis-related molecules were significantly involved in the occurrence of AAA. Utilizing multiple machine learning algorithms, eight key PRGs (cysteinyl aspartate specific proteinase [CASP]1, infiltrating lymphocyte [IL]1B, IL18

关键词： abdominal aortic aneurysm cytosolic DNA sensing pathway hsa-miR-331-3p machine learning algorithms pyroptosis TNF

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Ensemble machine learning models for sperm quality evaluation concerning success rate of clinical pregnancy in assisted reproductive techniques

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SCIENTIFIC REPORTS 2024年第1期14卷 1-10页

作者： Mehrjerd, Ameneh Dehghani, Toktam Jajroudi, Mahdie Eslami, Saeid Rezaei, Hassan Ghaebi, Nayyereh Khadem Univ Med Greifswald Fac Med Dept Psychiat & Psychotrop Greifswald Germany Mashhad Univ Med Sci Fac Med Dept Med Informat Mashhad Iran Univ Sistan & Baluchestan Dept Math Stat & Comp Sci Zahedan Iran Mashhad Univ Med Sci Inst Pharmaceut Technol Pharmaceut Res Ctr Mashhad Iran Univ Amsterdam Dept Med Informat Amsterdam UMC Locat AMC Amsterdam Netherlands Mashhad Univ Med Sci Sch Med Dept Obstet & Gynecol Mashhad Iran

This study aimed to investigate the influence of various sperm quality characteristics, including morphology, motility, and count, on the success rates of clinical pregnancy achieved through assisted reproductive technologies (ART) such as in-vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and intrauterine insemination (IUI). The secondary objective was to assess the impact of these sperm parameters on the clinical pregnancy rate that resulted in the detection of a fetal heartbeat during the 11th week of gestation, a crucial milestone in successful ART-derived pregnancies. The researchers employed a retrospective analysis, evaluating data from 734 couples undergoing IVF/ICSI and 1197 couples undergoing IUI across two infertility centers. Exclusion criteria included cases involving donated eggs or sperm, surrogate uteri, and infertile couples with combined male and female factors. Five ensemble machine-learning models were utilized to predict the clinical pregnancy success rates. The Random Forest (RF) model achieved the highest mean accuracy (0.72) and area under the curve (AUC) (0.80), outperforming the other models for both IVF/ICSI and IUI procedures. The Shapley Additive Explanations (SHAP) value analysis revealed that for IUI cycles, all three sperm parameters (morphology, motility, and count) had significant negative impacts on the prediction of clinical pregnancy success. In contrast, for IVF/ICSI cycles, sperm motility had a positive effect, while sperm morphology and count were negative factors. In cycles with 1 to 5 retrieved eggs, sperm motility, and count, they positively affected the clinical pregnancy rate. The study also identified cut-off values for sperm count, with 54 and 35 being the respective thresholds for IVF/ICSI and IUI. Additionally, a significant cut-off point 30 was found for the sperm morphology parameter across all procedures. This study underscores the immense potential of leveraging ensemble machine learning models

关键词： Infertility Sperm quality IVF ICSI IUI machine learning algorithms

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Resource Allocation in V2X Networks: From Classical Optimization to machine learning-Based Solutions

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 1958-1974页

作者： Parvini, Mohammad Schulz, Philipp Fettweis, Gerhard Tech Univ Dresden Vodafone Chair Mobile Commun Syst D-01062 Dresden Germany

As one of the promising intelligent transportation frameworks, vehicular platooning has the potential to bring about sustainable and efficient mobility solutions. One of the challenges in the development of platooning is maintaining the string stability, which ensures that there is no amplification of the signal of interest along the platoon chain. String stability is dependent on reliable inter-vehicle communications and proper controller design. Therefore, in this paper, we formulate radio resource management (RRM) problem with the purpose of satisfying the reliability of the vehicle-to-vehicle (V2V) links and string stability of the platoon. We tackle the optimization problem from different angles. First, we devise centralized classical approaches based on difference of two convex functions (d.c.) programming, in which we assume the base station (BS) has full knowledge over the V2V channel gains. In the second strategy, we develop decentralized resource allocation approaches based on multi-agent reinforcement learning (MARL). In essence, we model each transmitter vehicle in the platoon as an autonomous agent that tries to find an optimal policy according to its local estimated information to maximize the total expected reward. We also investigate whether the integration of federated learning (FL) with decentralized MARL algorithms can bring any potential benefits. This comparison between classical and machine learning (ML)-based RRM strategies helps us make crucial observations in terms of robustness, sensitivity, and efficacy of the policies that are learned by reinforcement learning (RL)-based resource allocation algorithms.

关键词： Resource management Reliability machine learning algorithms 3GPP Reinforcement learning Prediction algorithms Power control Difference of two convex functions (d.c.) programming optimization multi-agent reinforcement learning (MARL) platooning radio resource management (RRM) federated learning (FL)

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Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Zhao, Yaowei Ma, Xiyuan Meng, Xianghong Li, Hongyu Tang, Qiang Heilongjiang Univ Chinese Med Harbin 150040 Heilongjiang Peoples R China Univ Chinese Med Affiliated Hosp Heilongjiang 2 Harbin 150000 Heilongjiang Peoples R China

This study employs machine learning and single-cell transcriptome sequencing (scRNA-seq) analysis to unearth novel biomarkers and delineate the immune characteristics of ischemic stroke (IS), thereby contributing fresh insights into IS treatment *** research leverages gene expression data sourced from the GEO database. We undertake weighted gene co-expression network analysis (WGCNA) to filter pertinent genes and subsequently employ machine learning algorithms for the identification of feature genes. Concurrently, we rigorously execute quality control measures, dimensionality reduction techniques, and cell annotation on the scRNA-seq data to pinpoint differentially expressed genes (DEGs). The identification of core genes, denoted as Hub genes, among the feature genes and DEGs, is achieved through meticulous overlapping analysis. We illuminate the immune characteristics of these Hub genes using a suite of analytical tools, encompassing CIBERSORT, MCPcounter, and pseudotemporal analysis, all based on immune cell annotations and single-cell transcriptome ***, we harness the CMap database to prognosticate potential therapeutic drugs and scrutinize their associations with the identified Hub genes. Our findings unveil robust linkages between three pivotal Hub genes-namely, RNF13, VASP, and CD163-and specific immune cell types such as T cells and neutrophils. These Hub genes predominantly manifest in macrophages and microglial cells within the scRNA-seq immune cell population, exhibiting variances across different stages of cellular differentiation. In conclusion, this study unearths highly pertinent biomarkers for IS diagnosis and elucidates IS-induced immune infiltration characteristics, thus providing a firm foundation for a comprehensive exploration of potential immune mechanisms and the identification of novel therapeutic targets for IS.

关键词： Ischemic stroke Immune features machine learning algorithms Molecular docking Pseudo-time analysis Single-cell transcriptomic sequencing

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machine learning Models for Predicting Permanent Deformation in Railway Tracks

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TRANSPORTATION GEOTECHNICS 2024年 47卷

作者： Ramos, Ana Correia, Antonio Gomes Nasrollahi, Kourosh Nielsen, Jens C. O. Calcada, Rui Univ Porto Fac Engn CONSTRUCT LESE Porto Portugal Univ Minho ISISE Guimaraes Portugal Univ Minho Dept Civil Engn Guimaraes Portugal Chalmers Univ Technol Dept Mech & Maritime Sci CHARMEC Gothenburg Sweden

To enhance track geometry maintenance planning and reduce infrastructure costs, accurate predictions of accumulated permanent track deformation (settlement) caused by cyclic loading of ballast and subgrade is crucial for railway infrastructure managers. This paper proposes a novel approach to predict long-term settlement with reduced computational cost, based on an extensive parameter study using a hybrid methodology to evaluate both short- and long-term track performance. Various machine learning techniques are compared and employed to develop predictive models, which are validated using measured results from a filed demonstrator of ballasted track. The performance and accuracy of each model are assessed using multiple metrics, and a sensitivity analysis is conducted to identify influential explanatory variables. Notably, the developed random forest model demonstrates good agreement with field measured settlement data. This approach bridges the gap between numerical simulation and empirical data, offering an improved holistic understanding of permanent track deformation. The methodology holds potential for implementation in a computational decision support system for railway track maintenance and renewal management.

关键词： machine learning algorithms Permanent deformation in railways Prediction Infrastructure Life-cycle management Validation

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Comparison of deep and conventional machine learning models for prediction of one supply chain management distribution cost

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Yu, Xiaomo Tang, Ling Long, Long Sina, Mohammad Nanning Normal Univ Dept Logist Management & Engn Nanning 530001 Guangxi Peoples R China Guangxi Minzu Univ Coll Arts Nanning 530001 Guangxi Peoples R China Nanning Normal Univ Coll Comp Sci & Informat Engn Nanning 530001 Guangxi Peoples R China Islamic Azad Univ Dept Petr Engn Omidiyeh Branch Omidiyeh Iran

Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it's essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources.

关键词： Supply Chain Management (SCM) Decision-making machine learning algorithms Deep learning Convolutional neural network

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Precipitation prediction in Bangladesh using machine learning approaches

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2024年第1期18卷 23-56页

作者： Islam, Md. Ariful Shampa, Mosa. Tania Alim Univ Dhaka Dept Robot & Mechatron Engn Dhaka 1000 Bangladesh Univ Dhaka Dept Oceanog Dhaka 1000 Bangladesh

In the assessment of different hydrological activities, the prediction of rainfall is essential. As agriculture is critical to survival in Bangladesh, rainfall or precipitation is most important. This study shows how a machine learning approach can be used to make a reliable model for predicting rain. This way, people can know when rain is coming and take the steps they need to protect their crops. Many techniques have been applied so far to predict rainfall. But machine learning algorithms can provide more accuracy in this case. Nine machine learning algorithms have been used to find a good model that can be used to predict rain in Bangladesh. The prediction models were evaluated by dint of evaluation metrics such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). Among nine algorithms and eight models, the model H including all meteorological exogenous inputs with gradient boosting regressor algorithm led to the best predictions (R-2 = 0.78, RMSE = 134, MAE = 92) for Sylhet division. The model G excluding wind speed with gradient boosting regressor algorithm shows the best predictions (R-2 = 0.76, RMSE = 147, MAE = 89) for both Chittagong and Rangpur divisions.

关键词： rainfall machine learning algorithms precipitation gradient boosting regressor GBR Bangladesh

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Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

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COGENT FOOD & AGRICULTURE 2024年第1期10卷

作者： Youssef, Mohamed A. Peters, R. Troy El-Shirbeny, Mohammed Abd-ElGawad, Ahmed M. Rashad, Younes M. Hafez, Mohamed Arafa, Yasser Ain Shams Univ Fac Agr Dept Agr Engn Cairo Egypt Washington State Univ Dept Biol Syst Engn Pullman WA USA Natl Author Remote Sensing & Space Sci NARSS Cairo Egypt Arab Org Agr Dev AOAD Cairo Egypt King Saud Univ Coll Food & Agr Sci Plant Prod Dept Riyadh Saudi Arabia City Sci Res & Technol Applicat SRTA City Arid Lands Cultivat Res Inst ALCRI Plant Protect & Biomol Diag Dept New Borg El Arab Egypt City Sci Res & Technol Applicat SRTA City Arid Lands Cultivat Res Inst ALCRI Land & Water Technol Dept New Burg El Arab Egypt

This study addressed the increasing challenges of climate change by exploring the use of machine learning (ML) algorithms to predict the reference evapotranspiration (ETo). Accurate ETo prediction is crucial for optimizing irrigation water management. This research aimed to assess the reliability and accuracy of ML algorithms in predicting ETo values. Three ETo calculation methods were employed: Penman-Monteith (PM), Hargreaves (HA), and Blaney-Criddle (BC). The study analyzed ETo and other climate variables using the modified Mann-Kendall test (m-MK) and Theil Sen's slope estimator methods to identify trends. Multiple ML algorithms, including Support Vector Regression (SVR), Random Forest (RF), XGboost, K-Nearest Neighbor (KNN), Decision Trees (DT), Linear Regression (LR), and Multiple Linear Regression (MLR) were utilized for ETo prediction. The ML algorithms exhibited excellent performance, with coefficients of determination (R-2) values ranging from 0.97 to 0.99 for PM, 0.99 for HA, and from 0.91 to 0.92 for BC. The models demonstrated a high value of the Kling-Gupta efficiency (KGE) with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. Strong correlations between the predicted and calculated daily ETo were observed with R-2 values of 0.99, 0.99, and 0.92 for PM, HA, and BC methods, respectively. In conclusion, this study affirmed the accuracy and reliability of ML algorithms to match that of standard ETo prediction equations.

关键词： Climate change reference evapotranspiration machine learning algorithms modified Mann-Kendall test Kling-Gupta efficiency

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