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machine-learning-Based Predictive Model for Bothersome Stress Urinary Incontinence Among Parous Women in Southeastern China

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INTERNATIONAL UROGYNECOLOGY JOURNAL 2025年第2期36卷 391-401页

作者： Wang, Qi Jiang, Xiaoxiang Li, Xiaoyan Que, Yanzhen Lin, Chaoqin Fujian Matern & Child Hlth Hosp Dept Gynecol 18 Dao Shan St Fuzhou 350000 Peoples R China Fujian Med Univ Coll Clin Med Obstet & Gynecol & Pediat Fuzhou Peoples R China Fujian Prov Key Lab Women & Childrens Crit Dis Res Fuzhou Peoples R China Shaxian Gen Hosp Dept Gynecol & Obstet Sanming Peoples R China

Introduction and HypothesisAccurate identification of female populations at high risk for urinary incontinence (UI) and early intervention are potentially effective initiatives to reduce the prevalence of UI. We aimed to apply machine-learning techniques to establish, internally validate, and provide interpretable risk assessment *** from a cross-sectional epidemiological survey of female urinary incontinence conducted in 2022 were used. Sociodemographic and obstetrics-related characteristics, comorbidities, and urinary incontinence questionnaire results were used to develop multiple prediction models. Seventy percent of the individuals in the study cohort were employed in model training, and the remainder were used for internal validation. Model performance was characterized by area under the receiver-operating characteristic curve (AUC) and calibration curves, as well as Brier scores. The best-performing model was finally selected to develop an online prediction *** results showed that bothersome stress urinary incontinence (BSUI) occurred in 9.6% (849 out of 8,830) of parous women. The XGBoost model achieved the best prediction performance (training set: AUC 0.796, 95% confidence interval [CI]: 0.778-0.815, validation set: AUC 0.720, 95% CI: 0.686-0.754). Additionally, the XGBoost model achieved the lowest (best) Brier score among the models, with sensitivity of 0.657, specificity of 0.690, accuracy of 0.688, positive predictive value of 0.231, and negative predictive value of 0.948. Based on this model, the top five risk factors for the development of BSUI among parous women were ranked as follows: body mass index, age, vaginal delivery, constipation, and maximum fetal birth weight. An online calculator was provided for clinical *** application of machine-learning algorithms provides an acceptable, though not perfect, prediction of BSUI risk among parous women, requiring further validation and improvement in future researc

关键词： Bothersome stress urinary incontinence Big data machine-learning algorithms Parous women Prediction model

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Comparison of Cloud-Mask algorithms and machine-learning Methods Using Sentinel-2 Imagery for Mapping Paddy Rice in Jianghan Plain

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REMOTE SENSING 2024年第7期16卷 1305页

作者： Gao, Xinyi Chi, Hong Huang, Jinliang Han, Yifei Li, Yifan Ling, Feng Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Key Lab Monitoring & Estimate Environm & Disaster Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Southern China, one of the traditional rice production bases, has experienced significant declines in the area of rice paddy since the beginning of this century. Monitoring the rice cropping area is becoming an urgent need for food security policy decisions. One of the main challenges for mapping rice in this area is the quantity of cloud-free observations that are vulnerable to frequent cloud cover. Another relevant issue that needs to be addressed is determining how to select the appropriate classifier for mapping paddy rice based on the cloud-masked observations. Therefore, this study was organized to quickly find a strategy for rice mapping by evaluating cloud-mask algorithms and machine-learning methods for Sentinel-2 imagery. Specifically, we compared four GEE-embedded cloud-mask algorithms (QA60, S2cloudless, CloudScore, and CDI (Cloud Displacement Index)) and analyzed the appropriateness of widely accepted machine-learning classifiers (random forest, support vector machine, classification and regression tree, gradient tree boost) for cloud-masked imagery. The S2cloudless algorithm had a clear edge over the other three algorithms based on its overall accuracy in evaluation and visual inspection. The findings showed that the algorithm with a combination of S2cloudless and random forest showed the best performance when comparing mapping results with field survey data, referenced rice maps, and statistical yearbooks. In general, the research highlighted the potential of using Sentinel-2 imagery to map paddy rice with multiple combinations of cloud-mask algorithms and machine-learning methods in a cloud-prone area, which has the potential to broaden our rice mapping strategies.

关键词： cloud-mask algorithms machine-learning algorithms paddy rice GEE Sentinel-2 imagery

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Data stream clustering for low-cost machines

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2022年 166卷 57-70页

作者： Cerin, Christophe Kimura, Keiji Sow, Mamadou Sorbonne Paris Nord Univ LIPN CNRS UMR 7030 99 Ave JB Clement F-93430 Villetaneuse France Waseda Univ Fac Sci & Engn Sch Fundamental Sci & Engn 3-4-1 OkuboShinjuku ku Tokyo 1698555 Japan

Nowadays, the operations performed by the Internet of Things (IoT) systems are no more trivial since they rely on more sophisticated devices than in the past. The IoT system is physically composed of connected computing, digital, mechanical devices such as sensors or actuators. Most of the time, each of them incorporates a logical arithmetic unit that can pre-compute or compute on the device. To extract value from the data produced at the edge, processing power offered by cloud computing is still utilized. However, streaming data to the cloud exposes some limitations related to the increased communication and data transfer, which introduces delays and consumes network bandwidth. Clustering data is one example of a treatment that can be executed in the cloud. In this paper, we propose a methodology for solving the data stream clustering problem at the edge. Data Stream clustering is defined as the clustering of data that arrive continuously, such as telephone records, multimedia data, sensors data, financial transactions, etc. Since we use low-cost and low-capacity devices, the objective is, given a sequence of points, to construct a good clustering of the stream using a small amount of memory and time. We propose a 'windowing' scheme, coupled with a sampling scheme to respect the objective. Under the experimental conditions, experiments show that the clustering solutions can be controlled, with difficulties for time-stamped data but not for random data or data with well-delimited clusters. The main advantage of our schema is that we are clustering data "on the fly" with no knowledge or assumption regarding the available data. We do not assume that all the data are known before a treatment batch by batch. Our schema also has the potential to be adapted to other classes of machine learning algorithms. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Edge AI machine-learning algorithms Online data stream clustering Experiments on heterogeneous and low cost hardware

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Development of a prediction model for head and neck volume reduction by clinical factors, dose-volume histogram parameters and radiomics in head and neck cancer^&DAG;

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JOURNAL OF RADIATION RESEARCH 2023年第5期64卷 783-794页

作者： Ishizawa, Miyu Tanaka, Shohei Takagi, Hisamichi Kadoya, Noriyuki Sato, Kiyokazu Umezawa, Rei Jingu, Keiichi Takeda, Ken Yamagata Univ Grad Sch Med Sci Dept Heavy Particle Med Sci 2-2-2 Iidanishi Yamagata 9902331 Japan Tohoku Univ Dept Radiat Oncol Grad Sch Med 1-1 Seiryo MachiAoba Ku Sendai Miyagi 9808574 Japan Tohoku Univ Sch Hlth Sci Dept Radiol Technol 1-1 Seiryo MachiAoba Ku Sendai Miyagi 9808575 Japan Tohoku Univ Hosp Dept Radiat Technol 1-1 Seiryo MachiAoba Ku Sendai Miyagi 9808574 Japan

In external radiotherapy of head and neck (HN) cancers, the reduction of irradiation accuracy due to HN volume reduction often causes a problem. Adaptive radiotherapy (ART) can effectively solve this problem;however, its application to all cases is impractical because of cost and time. Therefore, finding priority cases is essential. This study aimed to predict patients with HN cancers are more likely to need ART based on a quantitative measure of large HN volume reduction and evaluate model accuracy. The study included 172 cases of patients with HN cancer who received external irradiation. The HN volume was calculated using cone-beam computed tomography (CT) for irradiation-guided radiotherapy for all treatment fractions and classified into two groups: cases with a large reduction in the HN volume and cases without a large reduction. Radiomic features were extracted from the primary gross tumor volume (GTV) and nodal GTV of the planning CT. To develop the prediction model, four feature selection methods and two machine-learning algorithms were tested. Predictive performance was evaluated by the area under the curve (AUC), accuracy, sensitivity and specificity. Predictive performance was the highest for the random forest, with an AUC of 0.662. Furthermore, its accuracy, sensitivity and specificity were 0.692, 0.700 and 0.813, respectively. Selected features included radiomic features of the primary GTV, human papillomavirus in oropharyngeal cancer and the implementation of chemotherapy;thus, these features might be related to HN volume change. Our model suggested the potential to predict ART requirements based on HN volume reduction .

关键词： radiotherapy head and neck cancer machine-learning algorithms volume reduction

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The Game of Drones: rapid agent-based machine-learning models for multi-UAV path planning

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COMPUTATIONAL MECHANICS 2020年第1期65卷 217-228页

作者： Zohdi, T. I. Univ Calif Berkeley Dept Mech Engn 6195 Etcheverry Hall Berkeley CA 94720 USA

The goal of this article is to provide basic modeling and simulation techniques for systems of multiple interacting Unmanned Aerial Vehicles, so called "swarms", for applications in mapping. Also, the paper illustrates the application of basic machine-learning algorithms to optimize their information gathering. Numerical examples are provided to illustrate the concepts.

关键词： Drones UAVs Simulation machine-learning algorithms

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Enhanced gate-valve failure detection in water distribution networks using ML and pressure data

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2024年第5期73卷 969-979页

作者： Kim, Hyunjun Jung, Kwangjun Lee, Sumin Jeong, Eunhye Pusan Natl Univ Dept Mech Engn 2 Busandaehak Ro 63 Beon-Gil Busan South Korea Flowork Lab Res & Dev Dept 8 Seojeon Ro Pusan South Korea

This study introduces an innovative diagnostic approach for identifying gate-valve failures in water distribution systems. By implementing high-frequency pressure sensors upstream and downstream of the gate valves, we obtained detailed pressure data that are pivotal for fault diagnosis. We explored three distinct machine-learning algorithms and two data-handling techniques to ensure optimal performance in real-world applications. In our methodology, supervised learning algorithms are used to analyze pressure differentials and predict valve behavior. We rigorously tested these algorithms using both raw and feature-engineered data, and the results indicated the effectiveness of the Gaussian-na & iuml;ve Bayes model with six extracted features. This approach enhances the precision and reliability of diagnostics in water distribution networks.

关键词： gate-valve diagnostics machine-learning algorithms network maintenance strategy pressure differential analysis supervised learning water distribution networks

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Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

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Geoscience Frontiers 2020年第4期11卷 1203-1217页

作者： Hamid Reza Pourghasemi Amiya Gayen Mohsen Edalat Mehrdad Zarafshar John P.Tiefenbacher Department of Natural Resources and Environmental Engineering College of AgricultureShiraz University71441-65186ShirazIran Department of Geography Ballygunge Science CollegeUniversity of CalcuttaKolkataWest Bengal700019India Crop Production and Plant Breeding Department School of AgricultureShiraz University71441-65186ShirazIran Natural Resources Department Fars Agricultural and Natural Resources Research and Education CenterAREEOShirazFarsIran Department of Geography Texas State UniversitySan MarcosTX78666USA

Natural hazards are often studied in ***,there is a great need to examine hazards holistically to better manage the complex of threats found in any *** regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is *** parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and *** effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and *** study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern *** do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these *** floods,landslides,and forest fires were surveyed and *** locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data *** conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and *** outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each *** areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,***,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to

关键词： Multi-hazard risk mapping Considering flood Landside and forest fire jointly machine-learning algorithms

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A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020年 363卷 112907-000页

作者： Zohdi, T. I. Univ Calif Berkeley Dept Mech Engn 6195 Etcheverry Hall Berkeley CA 94720 USA

The objective of this work is to illustrate how to algorithmically integrate machine-learning algorithms (MLA's) with multistage/multicomponent fire spread models. In order to tangibly illustrate this process, this work develops a framework for a specific model problem combining: (I) a meshless discrete element "submodel" that tracks the trajectory of airborne hot particles/embers, subject to prevailing wind velocities and updrafts, (II) a topographical "submodel" of the ambient combustible material whereby airborne embers that make contact are allowed to start secondary fires (if conditions are appropriate), combined with ground-based surface spread and burn rates for generating new embers, new updrafts (due to hot air), etc., and (III) a machine-learning Algorithm to rapidly ascertain the multi-submodel system parameters that force the overall model to match observations. The submodels compute both ground and airborne hot-ember driven fire propagation, as well as subsequent distribution of debris/soot, which is important for air-quality assessment. The overall framework is designed for use in digital twin technology, which refers to an adaptive digital replica of a physical system, whereby model updates are continuously in near real-time. This necessitates a rapid simulation paradigm that can easily interface with telecommunications, cameras and sensors. The presented framework is designed to run quickly on laptops and hand held devices, with the guiding principle being to make it potentially useful for first-responders in real-time. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Fire-propagation Ember flow Digital-twin machine-learning algorithms

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Investigations on the Use of Ensemble Methods for Specification-Oriented Indirect Test of RF Circuits

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2020年第2期36卷 189-203页

作者： El Badawi, H. Azais, Florence Bernard, S. Comte, M. Kerzerho, V Lefevre, F. Univ Montpellier CNRS LIRMM 161 Rue Ada F-34095 Montpellier France NXP Semicond 2 Esplanade Anton Phillips F-14000 Caen France

In order to reduce the costs of industrial testing of analog and Radio Frequency (RF) integrated circuits, a widely studied solution is indirect testing. Indeed, indirect testing is based on learning-machine algorithms to train a regression model that links the space of low-cost indirect measurements to the space of performance parameters guaranteed by datasheets, thus relaxing the constraints on expensive test equipment. This article explores the potential benefit of using ensemble learning in this context. Unlike traditional learning models that use a single model to estimate targeted parameters, ensemble-learning models involve training several individual regression models and combining their outputs to improve the predictive power of the ensemble model. Different ensemble methods based on bagging, boosting or stacking are investigated and compared to classical individual models. Experiments are performed on three RF performances of a LNA for which we have production test data and model quality is discussed in terms of goodness-of-fit, accuracy and reliability. The influence of the training set size is also explored. Finally, the efficiency of classical and ensemble models is compared in the context of a two-tier test flow that permits to tradeoff test cost and test quality.

关键词： Indirect testing RF integrated circuits machine-learning algorithms Ensemble methods Test efficiency

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Logical Analysis of Data in Structure-Activity Investigation of Polymeric Gene Delivery

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MACROMOLECULAR THEORY AND SIMULATIONS 2011年第4期20卷 275-285页

作者： Gubskaya, Anna V. Bonates, Tiberius O. Kholodovych, Vladyslav Hammer, Peter Welsh, William J. Langer, Robert Kohn, Joachim Mt St Vincent Univ Dept Chem & Phys Halifax NS B3M 2J6 Canada Rutgers State Univ RUTCOR Piscataway NJ 08854 USA UMDNJ RWJMS Dept Pharmacol Piscataway NJ 08854 USA MIT Dept Chem Engn Cambridge MA 02139 USA Rutgers State Univ New Jersey Ctr Biomat Piscataway NJ 08854 USA

To date semi-empirical or surrogate modeling has demonstrated great success in the prediction of the biologically relevant properties of polymeric materials. For the first time, a correlation between the chemical structures of poly(beta-amino esters) and their efficiency in transfecting DNA was established using the novel technique of logical analysis of data ( LAD). Linear combination and explicit representation models were introduced and compared in the framework of the present study. The most successful regression model yielded satisfactory agreement between the predicted and experimentally measured values of transfection efficiency (Pearson correlation coefficient, 0.77;mean absolute error, 3.83). It was shown that detailed analysis of the rules provided by the LAD algorithm offered practical utility to a polymer chemist in the design of new biomaterials.

关键词： combinatorial library computational modeling machine-learning algorithms polymeric gene delivery prediction of biological response

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