Artigos de revistas sobre o tema "Disease Prediction and Monitoring Modelling"
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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.
Texto completo da fonteWang, 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.
Texto completo da fonteSharma, 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.
Texto completo da fonteKAIMI, 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.
Texto completo da fonteVychuzhanin, 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.
Texto completo da fonteAlodat, 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.
Texto completo da fonteDixon, 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.
Texto completo da fonteLiebenstund, 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.
Texto completo da fonteEswaran, 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.
Texto completo da fonteCheng, 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.
Texto completo da fonteKulkarni, 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.
Texto completo da fonteLuisa, 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.
Texto completo da fonteSethy, 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.
Texto completo da fonteJones, 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.
Texto completo da fonteNwobodo-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.
Texto completo da fonteVelasquez-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.
Texto completo da fonteHelget, 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.
Texto completo da fonteZhao, 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.
Texto completo da fonteJombart, 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.
Texto completo da fonteMasih, 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.
Texto completo da fontePrzybilla, 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.
Texto completo da fonteANDERSON, 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.
Texto completo da fonteChua, 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.
Texto completo da fonteLin, 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.
Texto completo da fonteShi, 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.
Texto completo da fonteSuzuki, 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.
Texto completo da fonteMrara, 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.
Texto completo da fonteVakitbilir, 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.
Texto completo da fonteSibarani, 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.
Texto completo da fonteThomas, 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.
Texto completo da fonteFerrari, 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.
Texto completo da fonteMorlighem, 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.
Texto completo da fonteRayner, 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.
Texto completo da fonteStefanescu, 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.
Texto completo da fonteHamid, 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.
Texto completo da fonteDrake, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteGerasimenko, 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.
Texto completo da fonteCowled, 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.
Texto completo da fonteBritton, 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.
Texto completo da fonteGlauche, 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.
Texto completo da fonteMaciukiewicz, 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.
Texto completo da fonteEjma-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.
Texto completo da fonteSkendžić, 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.
Texto completo da fonteSánchez-pérez, Isabel, Jorge Melones Herrero, Alicia Villacampa, et al. "P160 MODELLING CARDIOVASCULAR TOXICITY IN CELLULO ASSOCIATED WITH ANTITUMORALS." Journal of Hypertension 42, Suppl 3 (2024): e119. http://dx.doi.org/10.1097/01.hjh.0001063512.42008.51.
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