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Journal articles on the topic 'Disease Prediction and Monitoring Modelling'

Author: Grafiati

Published: 12 October 2024

Last updated: 31 July 2025

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1

Orakwue, Stella I., and Nkolika O. Nwazor. "Plant Disease Detection and Monitoring Using Artificial Neural Network." International Journal of Scientific Research and Management 10, no. 01 (2022): 715–22. http://dx.doi.org/10.18535/ijsrm/v10i1.ec01.

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Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested valid

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Wang, Y. P., N. H. Idris, F. M. Muharam, N. Asib, and Alvin M. S. Lau. "Comparison of different variable selection methods for predicting the occurrence of Metisa Plana in oil palm plantation using machine learning." IOP Conference Series: Earth and Environmental Science 1274, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1755-1315/1274/1/012008.

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Abstract Monitoring and predicting the spatio-temporal distribution of crop pests and assessing related risks are crucial for effective pest management strategies. Machine learning techniques have shown potential in analysing agricultural data and providing accurate predictions. Variable selection plays a critical role in crop pest analysis by identifying the most informative and influential features that contribute to pest distribution and risk prediction. The current practice of choosing variable selection methods is mostly based on previous experience and may involve a certain degree of sub

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Sharma, V., S. K. Ghosh, and S. Khare. "A PROPOSED FRAMEWORK FOR SURVEILLANCE OF DENGUE DISEASE AND PREDICTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 317–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-317-2023.

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Abstract. Recurring outbreaks of dengue during past decades have affected public health and burdened resource constraint health systems across the world. Transmission of such diseases is a conjugation of various complex factors including vector dynamics, transmission mechanism, environmental conditions, cultural behaviours, and public health policies. Modelling and predicting early outbreaks is the key to an effective response to control the spread of disease. In this study, a comprehensive framework has been proposed to model dengue disease by integrating significant factors using different i

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KAIMI, I., and P. J. DIGGLE. "A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections." Epidemiology and Infection 139, no. 12 (2011): 1854–62. http://dx.doi.org/10.1017/s0950268811000057.

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SUMMARYThe AEGISS (Ascertainment and Enhancement of Disease Surveillance and Statistics) project uses spatio-temporal statistical methods to identify anomalies in the incidence of gastrointestinal infections in the UK. The focus of this paper is the modelling of temporal variation in incidence using data from the Southampton area in southern England. We identified and fitted a hierarchical stochastic model for the time series of daily incident cases to enable probabilistic prediction of temporal variation in risk, and demonstrated the resulting gains in predictive accuracy by comparison with a

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Vychuzhanin, Vladimir, Alexey Vychuzhanin, Olga Guzun, and Oleg Zadorozhny. "Mathematical modelling of eye condition in glaucoma: Approaches to parameter analysis and their interactions." Information Technology and Computer Engineering 22, no. 1 (2025): 10–19. https://doi.org/10.63341/vitce/1.2025.09.

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Mathematical modelling of physiological processes is a key component of intelligent medical systems, as it describes disease mechanisms in greater detail and contributes to early diagnosis. This study presents an analytical model for assessing eye health, incorporating key ophthalmological parameters: intraocular pressure (IOP), perfusion coefficient (Pperf), best-corrected visual acuity (BCVA), visual field index (VFI), retinal nerve fibre layer thickness (RNFL), and neuroretinal rim area (Rim_area). The study aimed to develop a model that can accurately evaluate the nonlinear interactions be

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Alodat, Iyas. "Analysing and predicting COVID-19 AI tracking using artificial intelligence." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 03 (2021): 2141005. http://dx.doi.org/10.1142/s1793962321410051.

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In this paper, we will discuss prediction methods to restrict the spread of the disease by tracking contact individuals via mobile application to individuals infected with the COVID-19 virus. We will track individuals using bluetooth technology and then we will save information in the central database when they are in touch. Monitoring cases and avoiding the infected person help with social distance. We also propose that sensors used by people to obtain blood oxygen saturation level and their body temperature will be used besides bluetooth monitoring. The estimation of the frequency of the dis

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Dixon, Giles, Hannah Thould, Matthew Wells, et al. "A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease." European Respiratory Review 34, no. 176 (2025): 240194. https://doi.org/10.1183/16000617.0194-2024.

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BackgroundThe unpredictable trajectory and heterogeneity of interstitial lung disease (ILDs) make prognostication challenging. Current prognostic indices and outcome measures have several limitations. Quantitative computed tomography (qCT) provides automated numerical assessment of CT imaging and has shown promise when applied to the prognostication and disease monitoring of ILD. This systematic review aims to highlight the current evidence underpinning the prognostic value of qCT in predicting outcomes in ILD.MethodsA comprehensive search of four databases (Medline, EMCare, Embase and CINAHL

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Liebenstund, Lisa, Mark Coburn, Christina Fitzner, et al. "Predicting experimental success: a retrospective case-control study using the rat intraluminal thread model of stroke." Disease Models & Mechanisms 13, no. 12 (2020): dmm044651. http://dx.doi.org/10.1242/dmm.044651.

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ABSTRACTThe poor translational success rate of preclinical stroke research may partly be due to inaccurate modelling of the disease. We provide data on transient middle cerebral artery occlusion (tMCAO) experiments, including detailed intraoperative monitoring to elaborate predictors indicating experimental success (ischemia without occurrence of confounding pathologies). The tMCAO monitoring data (bilateral cerebral blood flow, CBF; heart rate, HR; and mean arterial pressure, MAP) of 16 animals with an ‘ideal’ outcome (MCA-ischemia), and 48 animals with additional or other pathologies (subdur

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Eswaran, Sarojini, Bharathiraj L.T, and Jayanthi S. "Modelling of ambient air quality, Coimbatore, India." E3S Web of Conferences 117 (2019): 00002. http://dx.doi.org/10.1051/e3sconf/201911700002.

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Air pollution is dispersion of the particulates, biological molecules, or other harmful materials into the Earth’s atmosphere, possibly causing diseases. Air pollutants can be either particles, liquids or gaseous. In the recent era, air pollution has become a major environmental issue because of the enhanced anthropogenic activities such as burning fossil fuels, natural gases, coal and oil, industrial process, advanced technologies and motor vehicles. The proposed project focused on air pollution study of North Coimbatore region (11° 0’ 16.4016’’ N and 76° 57’ 41.8752’’ E), Tamilnadu, India. T

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Cheng, Yunyun, Rong Cheng, Ting Xu, Xiuhui Tan, and Yanping Bai. "Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review." Bioengineering 12, no. 5 (2025): 514. https://doi.org/10.3390/bioengineering12050514.

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COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and

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Kulkarni, Mrunalini Harish, Chaitanya Kulkarni, K. Suresh Babu, Saima Ahmed Rahin, Shweta Singh, and D. Dinesh Kumar. "Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT." Scientific Programming 2022 (May 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/3603238.

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As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear

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Luisa, Fernanda Velasquez, Otero Marta, Basile Boris, Pijuan Josep, and Corrado Giandomenico. "Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards." Microorganisms 11(1), Feature Collection in Environmental Microbiology Section (2022): 73. https://doi.org/10.3390/microorganisms11010073.

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Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized

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Jones, K. L., R. C. A. Thompson, and S. S. Godfrey. "Social networks: a tool for assessing the impact of perturbations on wildlife behaviour and implications for pathogen transmission." Behaviour 155, no. 7-9 (2018): 689–730. http://dx.doi.org/10.1163/1568539x-00003485.

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Abstract Wildlife are increasingly subject to perturbations, which can impact pathogen transmission and lead to disease emergence. While a myriad of factors influence disease dynamics in wildlife, behaviour is emerging as a major influence. In this review, we examine how perturbations alter the behaviour of individuals and how, in turn, disease transmission may be impacted, with a focus on the use of network models as a powerful tool. There are emerging hypotheses as to how networks respond to different types of perturbations. The broad effects of perturbations make predicting potential outcom

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Sethy, Prabira Kumar, Santi Kumari Behera, Nithiyakanthan Kannan, Sridevi Narayanan, and Chanki Pandey. "Smart paddy field monitoring system using deep learning and IoT." Concurrent Engineering 29, no. 1 (2021): 16–24. http://dx.doi.org/10.1177/1063293x21988944.

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Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained

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Nwobodo-Nzeribe, N. H., H. U. Odo, and P. C. Ozoemena. "Predictive Model for the Monitoring and Detection of Heart Disease using Wavelet Based Machine Learning Technique." American Journal of Applied Science and and Engineering 3, no. 5 (2022): 1–12. https://doi.org/10.5281/zenodo.7153890.

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<strong>ABSTRACT</strong> <em>This research paper on Predictive Model for the monitoring and detection of heart disease using wavelet-based machine learning technique is aimed at enhancing and easing the process of detecting heart diseases efficiently and in an automated manner. This paper adopts the Dynamic Systems Development Model (DSDM) methodology which was originally based on the Rapid Application Development Methodology. This methodology is applied for a fast delivery of the new system within a specified work plan, budget and time. The methodology also features iterative phases such as

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Velasquez-Camacho, Luisa, Marta Otero, Boris Basile, Josep Pijuan, and Giandomenico Corrado. "Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards." Microorganisms 11, no. 1 (2022): 73. http://dx.doi.org/10.3390/microorganisms11010073.

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Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized

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Helget, Lindsay N., David J. Dillon, Bethany Wolf, et al. "Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis." Lupus Science & Medicine 8, no. 1 (2021): e000489. http://dx.doi.org/10.1136/lupus-2021-000489.

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ObjectiveLupus nephritis (LN) is an immune complex-mediated glomerular and tubulointerstitial disease in patients with SLE. Prediction of outcomes at the onset of LN diagnosis can guide decisions regarding intensity of monitoring and therapy for treatment success. Currently, no machine learning model of outcomes exists. Several outcomes modelling works have used univariate or linear modelling but were limited by the disease heterogeneity. We hypothesised that a combination of renal pathology results and routine clinical laboratory data could be used to develop and to cross-validate a clinicall

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Zhao, Hongwei, Naveed N. Merchant, Alyssa McNulty, et al. "COVID-19: Short term prediction model using daily incidence data." PLOS ONE 16, no. 4 (2021): e0250110. http://dx.doi.org/10.1371/journal.pone.0250110.

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Background Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series inter

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Jombart, Thibaut, Stéphane Ghozzi, Dirk Schumacher, et al. "Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1829 (2021): 20200266. http://dx.doi.org/10.1098/rstb.2020.0266.

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As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive

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Masih, Adven, and Alexander N. Medvedev. "Evaluating the performance of support vector machines based on different kernel methods for forecasting air pollutants." Вестник ВГУ. Серия: Системный анализ и информационные технологии, no. 3 (September 30, 2020): 5–14. http://dx.doi.org/10.17308/sait.2020.3/3035.

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The alarming level of air pollution in urban centres is an urgent threat to human health. Its consequences can be measured in terms of health issues experienced by children, an increasing numbers of heart and lung diseases, and, most importantly, the number of pollution related deaths. That is why a lot of attention has recently been paid to air pollution monitoring and prediction modelling. In order to develop prediction models, the study uses Support Vector Machines (SVM) with linear, polynomial, radial base function, normalised polynomial, and Pearson VII function kernels to predict the hou

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Przybilla, Jens, Peter Ahnert, Holger Bogatsch, et al. "Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia." Journal of Clinical Medicine 9, no. 2 (2020): 393. http://dx.doi.org/10.3390/jcm9020393.

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Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagno

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ANDERSON, D. P., D. S. L. RAMSEY, G. NUGENT, et al. "A novel approach to assess the probability of disease eradication from a wild-animal reservoir host." Epidemiology and Infection 141, no. 7 (2013): 1509–21. http://dx.doi.org/10.1017/s095026881200310x.

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SUMMARYSurveying and declaring disease freedom in wildlife is difficult because information on population size and spatial distribution is often inadequate. We describe and demonstrate a novel spatial model of wildlife disease-surveillance data for predicting the probability of freedom of bovine tuberculosis (caused by Mycobacterium bovis) in New Zealand, in which the introduced brushtail possum (Trichosurus vulpecula) is the primary wildlife reservoir. Using parameters governing home-range size, probability of capture, probability of infection and spatial relative risks of infection we employ

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Chua, Felix, Rama Vancheeswaran, Adrian Draper, et al. "Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score." Thorax 76, no. 7 (2021): 696–703. http://dx.doi.org/10.1136/thoraxjnl-2020-216425.

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IntroductionRisk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.MethodsMultivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).Results983 patients (median age 70, IQR 53–83; in-hospital mortalit

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Lin, Lingmin, Kailai Liu, Huan Feng, et al. "Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying." Mathematical Biosciences and Engineering 19, no. 10 (2022): 10096–107. http://dx.doi.org/10.3934/mbe.2022472.

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<abstract> <p>Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently and economically, an intelligent prediction of glucose is demanding. A glucose trajectory prediction system based on subcutaneous interstitial continuous glucose monitoring data and deep learning models for ensuing glucose trajectory was constructed, followed by the application of personalised prediction models on one parti

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Shi, Lei, Xiaoliang Feng, Longxing Qi, Yanlong Xu, and Sulan Zhai. "Modeling and Predicting the Influence of PM2.5 on Children’s Respiratory Diseases." International Journal of Bifurcation and Chaos 30, no. 15 (2020): 2050235. http://dx.doi.org/10.1142/s0218127420502351.

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In this paper, the influence of PM[Formula: see text] on children’s respiratory diseases is taken as the main research focus. Based on the real monitoring data of children’s respiratory diseases in Anhui province, the traditional model is modified substantially, leading to the establishment of two mathematical models. First of all, considering that the PM[Formula: see text] changes over time, a nonautonomous air pollution-related disease model is constructed to study its permanence and extinction. Furthermore, regarding lag days of PM[Formula: see text] exposure, an air pollution-related disea

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Suzuki, Ayako, and Hiroshi Nishiura. "Transmission dynamics of varicella before, during and after the COVID-19 pandemic in Japan: a modelling study." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5998–6012. http://dx.doi.org/10.3934/mbe.2022280.

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<abstract> <p>Public health and social measures (PHSMs) targeting the coronavirus disease 2019 (COVID-19) pandemic have potentially affected the epidemiological dynamics of endemic infectious diseases. In this study, we investigated the impact of PHSMs for COVID-19, with a particular focus on varicella dynamics in Japan. We adopted the susceptible-infectious-recovered type of mathematical model to reconstruct the epidemiological dynamics of varicella from Jan. 2010 to Sep. 2021. We analyzed epidemiological and demographic data and estimated the within-year and multi-year component

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Mrara, Busisiwe, Fathima Paruk, Constance Sewani-Rusike, and Olanrewaju Oladimeji. "Development and validation of a clinical prediction model of acute kidney injury in intensive care unit patients at a rural tertiary teaching hospital in South Africa: a study protocol." BMJ Open 12, no. 7 (2022): e060788. http://dx.doi.org/10.1136/bmjopen-2022-060788.

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IntroductionAcute kidney injury (AKI) is a decline in renal function lasting hours to days. The rising global incidence of AKI, and associated costs of renal replacement therapy, is a public health priority. With the only therapeutic option being supportive therapy, prevention and early diagnosis will facilitate timely interventions to prevent progression to chronic kidney disease. While many factors have been identified as predictive of AKI, none have shown adequate sensitivity or specificity on their own. Many tools have been developed in developed-country cohorts with higher rates of non-co

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Vakitbilir, Nuray, Abrar Islam, Alwyn Gomez, et al. "Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies." Sensors 24, no. 24 (2024): 8148. https://doi.org/10.3390/s24248148.

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Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to

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Sibarani, Imelda Juliana Br, Katherina Meylda Loy S, and Suharjito Suharjito. "Enhancing Predictive Accuracy for Differentiated Thyroid Cancer (DTC) Recurrence Through Advanced Data Mining Techniques." TIN: Terapan Informatika Nusantara 5, no. 1 (2024): 11–22. http://dx.doi.org/10.47065/tin.v5i1.5237.

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Thyroid cancer is becoming more common, and its 20% recurrence rate of which almost half are discovered more than five years after surgery, highlights how difficult it is to distinguish between a true disease relapse and chronic disease brought on by insufficient initial treatment. This ambiguity highlights the complicated dynamics that drive the mortality rates in patients with thyroid cancer. The purpose of this study is to be refining these predictions to control Differentiated Thyroid Cancer recurrence and minimize the risk of recurrence. The dataset was obtained by monitoring a total of 3

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Thomas, Charlotte M., Joseph F. Standing, Catherine Smith, et al. "BT34 Minimizing drug exposure in psoriasis using a therapeutic drug monitoring dashboard." British Journal of Dermatology 191, Supplement_1 (2024): i204—i205. http://dx.doi.org/10.1093/bjd/ljae090.431.

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Abstract There are growing numbers of individuals with psoriasis who have clear or nearly clear skin (i.e. disease control) on high-cost biologic drugs. They currently continue biologic treatment indefinitely, resulting in a long-term drug and healthcare burden. Prior research indicates that some sustain disease control on less frequent doses than the current ‘one-size-fits-all’ standard dosing regimen, particularly those with higher serum drug concentrations. This indicates a role for therapeutic drug monitoring (TDM) in enabling personalized drug minimization strategies. We sought to develop

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Ferrari, Simone, Alessandro Santus, and Luca Tendas. "Validation of a numerical software for the simulation of the pollutant dispersion from traffic in a real case: Some preliminary results." EPJ Web of Conferences 299 (2024): 01010. http://dx.doi.org/10.1051/epjconf/202429901010.

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An increasing attention of citizens and policy-makers is devoted to the monitoring and modelling of urban traffic-related air pollution (TRAP), as there is a demonstrated relationship among this and human health effects (e.g. circulatory and ischemic heart diseases, lung cancer, asthma onset in children and adults, and acute lower respiratory infections in children). In this work, we investigate the capability of the ENVI- met® software to reproduce the concentrations of pollutants, emitted from vehicular traffic, and the meteorological parameters, both measured by a specific monitoring statio

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Morlighem, Camille, Chibuzor Christopher Nnanatu, Corentin Visée, Atoumane Fall, and Catherine Linard. "Spatial interpolation of health and demographic variables: Predicting malaria indicators with and without covariates." PLOS One 20, no. 5 (2025): e0322819. https://doi.org/10.1371/journal.pone.0322819.

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Accurate mapping and disaggregation of key health and demographic risk factors have become increasingly important for disease surveillance, which can reveal geographical social inequalities for improved health interventions and for monitoring progress on relevant Sustainable Development Goals (SDGs). Household surveys like the Demographic and Health Surveys have been widely used as a proxy for mapping SDG-related household characteristics. However, there is no consensus on the workflow to be used, and different methods have been implemented with varying complexities. This study aims to compare

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Rayner, Fiona, Shaun Hiu, Andrew Melville, et al. "Clinical predictors of flare and drug-free remission in rheumatoid arthritis: preliminary results from the prospective BIO-FLARE experimental medicine study." BMJ Open 15, no. 4 (2025): e092478. https://doi.org/10.1136/bmjopen-2024-092478.

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Objectives Huge advances in rheumatoid arthritis (RA) treatment mean an increasing number of patients now achieve disease remission. However, long-term treatments can carry side effects and associated financial costs. In addition, some patients still experience painful and debilitating disease flares, the mechanisms of which are poorly understood. High rates of flare and a lack of effective prediction tools can limit attempts at treatment withdrawal. The BIOlogical Factors that Limit sustAined Remission in rhEumatoid arthritis (BIO-FLARE) experimental medicine study was designed to study flare

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Stefanescu, Simona, Relu Cocoș, Adina Turcu-Stiolica, et al. "Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results." Pathogens 10, no. 7 (2021): 789. http://dx.doi.org/10.3390/pathogens10070789.

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Pro-inflammatory mediators play an important role in the pathogenesis of pulmonary tuberculosis. Consecutively, 26 pulmonary tuberculosis patients were enrolled in our study based on the exclusion criteria. We have used Spearman’s correlation analysis, hierarchical clustering and regression modelling to evaluate the association of 11 biomarkers with culture status after antituberculosis treatment. The results of our study demonstrated that six inflammatory biomarkers of 11, C-reactive protein (CRP), white blood cells (WBC), neutrophils, interferon gamma inducible protein 10, C-reactive protein

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Hamid, Saima, S. S. Pathania, Tamjeeda Nisar, et al. "Unveiling Pest Resilience in Bottle Gourd: A Comprehensive Review of Field Monitoring and Statistical Insights." UTTAR PRADESH JOURNAL OF ZOOLOGY 46, no. 13 (2025): 1–9. https://doi.org/10.56557/upjoz/2025/v46i135079.

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Bottle gourd (genus Lagenaria, species siceraria) is one of valuable cucurbitaceous vegetables grown all over the world due to its nutritional and economic importance. But it is affected by several insect pests and diseases, resulting in considerable yield losses. The objective of this review is to provide a state-of-the art knowledge of the pocedures for bottle gourd pest monitoring, field screening, fruit damage assessment, rating system, antixenosis response as well as statistical correlation and regression analysis to obtain a deeper insight into the resistance mechanisms and yield loss pr

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Drake, Wonder P., Connie Hsia, Lobelia Samavati, et al. "Risk Indicators of Sarcoidosis Evolution-Unified Protocol (RISE-UP): protocol for a multi-centre, longitudinal, observational study to identify clinical features that are predictive of sarcoidosis progression." BMJ Open 13, no. 4 (2023): e071607. http://dx.doi.org/10.1136/bmjopen-2023-071607.

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IntroductionSarcoidosis is a pulmonary and systemic granulomatous disease with a wide range of potential outcomes, from spontaneous resolution to end-stage organ damage and death. Currently, clinicians have no easy-to-use risk stratification tools for important clinical outcomes in sarcoidosis, such as progressive lung disease. This study will address two clinical practice needs: (1) development of a risk calculator that provides an estimate of the likelihood of pulmonary progression in sarcoidosis patients during the follow-up period and (2) determine the optimal interval for serial clinical

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Kissi, Solomon Agyiri, Md Golam Muttaquee Talukder, and Muhammad Zahid Iqbal. "Data-Driven Predictive Modelling of Lifestyle Risk Factors for Cardiovascular Health." Electronics 14, no. 14 (2025): 2906. https://doi.org/10.3390/electronics14142906.

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Cardiovascular disease (CVD) remains the foremost global cause of mortality, driven significantly by modifiable lifestyle factors. This study employs a data-driven approach to identify and evaluate these risk factors using advanced machine learning techniques. Analysing a large publicly available dataset of over 300,000 adult health records containing lifestyle behaviours, clinical risk factors, and self-reported health indicators, this research implemented traditional classifiers, ensemble methods, and deep learning architectures to examine the impact of behaviours such as smoking, diet, phys

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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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40

Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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41

Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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42

Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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43

Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Abstract:

Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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44

Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.

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Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus. Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in t

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Cowled, Brendan D., Fiona Giannini, Sam D. Beckett, et al. "Feral pigs: predicting future distributions." Wildlife Research 36, no. 3 (2009): 242. http://dx.doi.org/10.1071/wr08115.

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Feral pig populations are expanding in many regions of the world following historically recent introductions. Populations are controlled to reduce damage to agriculture and the environment, and are also a recreational hunting resource. Knowledge of the area over which feral pigs may expand in the future could be used regionally to assist biosecurity planning, control efforts and the protection of biodiversity assets. The present study sought to estimate the future distribution of a recently introduced, expanding feral pig population in the remote Kimberley region of north-western Australia. An

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Britton, Tom, and Gianpaolo Scalia Tomba. "Estimation in emerging epidemics: biases and remedies." Journal of The Royal Society Interface 16, no. 150 (2019): 20180670. http://dx.doi.org/10.1098/rsif.2018.0670.

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When analysing new emerging infectious disease outbreaks, one typically has observational data over a limited period of time and several parameters to estimate, such as growth rate, the basic reproduction numberR0, the case fatality rate and distributions of serial intervals, generation times, latency and incubation times and times between onset of symptoms, notification, death and recovery/discharge. These parameters form the basis for predicting a future outbreak, planning preventive measures and monitoring the progress of the disease outbreak. We study inference problems during the emerging

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Glauche, Ingmar, Hendrik Liebscher, Christoph Baldow, et al. "A New Computational Method to Predict Long-Term Minimal Residual Disease and Molecular Relapse after TKI-Cessation in CML." Blood 128, no. 22 (2016): 3099. http://dx.doi.org/10.1182/blood.v128.22.3099.3099.

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Abstract Predicting minimal residual disease (MRD) levels in tyrosine kinase inhibitor (TKI)-treated chronic myeloid leukemia (CML) patients is of major clinical relevance. The reason is that residual leukemic (stem) cells are the source for both, potential relapses of the leukemicclone but also for its clonal evolution and, therefore, for the occurrence of resistance. The state-of-the art method for monitoring MRD in TKI-treated CML is the quantification of BCR-ABL levels in the peripheral blood (PB) by PCR. However, the question is whether BCR-ABL levels in the PB can be used as a reliable e

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Maciukiewicz, M., J. Schniering, H. Gabrys, et al. "OP0150 MACHINE LEARNING APPROACHES FOR RISK MODELLING IN INTERSTITIAL LUNG DISEASE ASSOCIATED WITH SYSTEMIC SCLEROSIS USING HIGH DIMENSIONAL IMAGE ANALYSIS." Annals of the Rheumatic Diseases 80, Suppl 1 (2021): 90. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2517.

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Background:The interstitial lung disease (ILD) associated with connective tissue diseases including systemic sclerosis (SSc) is heterogenous disease characterized by reduced survival of approximately 3 years (1). “Radiomics’’ is a field of research which describes the in-depth analysis of tissues by computational retrieval of high-dimensional quantitative features from medical images (2). Our previous study suggested capacity of radiomics features to differentiate between “high” and “low” risk groups for lung function decline in two independent cohorts (3).Objectives: •bTo develop robust, mach

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Ejma-Multański, Adam, Anna Wajda, and Agnieszka Paradowska-Gorycka. "Cell Cultures as a Versatile Tool in the Research and Treatment of Autoimmune Connective Tissue Diseases." Cells 12, no. 20 (2023): 2489. http://dx.doi.org/10.3390/cells12202489.

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Cell cultures are an important part of the research and treatment of autoimmune connective tissue diseases. By culturing the various cell types involved in ACTDs, researchers are able to broaden the knowledge about these diseases that, in the near future, may lead to finding cures. Fibroblast cultures and chondrocyte cultures allow scientists to study the behavior, physiology and intracellular interactions of these cells. This helps in understanding the underlying mechanisms of ACTDs, including inflammation, immune dysregulation and tissue damage. Through the analysis of gene expression patter

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Skendžić, Sandra, Monika Zovko, Ivana Pajač Živković, Vinko Lešić, and Darija Lemić. "The Impact of Climate Change on Agricultural Insect Pests." Insects 12, no. 5 (2021): 440. http://dx.doi.org/10.3390/insects12050440.

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Climate change and global warming are of great concern to agriculture worldwide and are among the most discussed issues in today’s society. Climate parameters such as increased temperatures, rising atmospheric CO2 levels, and changing precipitation patterns have significant impacts on agricultural production and on agricultural insect pests. Changes in climate can affect insect pests in several ways. They can result in an expansion of their geographic distribution, increased survival during overwintering, increased number of generations, altered synchrony between plants and pests, altered inte

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