Artículos de revistas sobre el tema "Disease Prediction and Monitoring Modelling"
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Orakwue, Stella I. y Nkolika O. Nwazor. "Plant Disease Detection and Monitoring Using Artificial Neural Network". International Journal of Scientific Research and Management 10, n.º 01 (3 de enero de 2022): 715–22. http://dx.doi.org/10.18535/ijsrm/v10i1.ec01.
Texto completoKAIMI, I. y P. J. DIGGLE. "A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections". Epidemiology and Infection 139, n.º 12 (9 de febrero de 2011): 1854–62. http://dx.doi.org/10.1017/s0950268811000057.
Texto completoWang, Y. P., N. H. Idris, F. M. Muharam, N. Asib y 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, n.º 1 (1 de diciembre de 2023): 012008. http://dx.doi.org/10.1088/1755-1315/1274/1/012008.
Texto completoSharma, V., S. K. Ghosh y 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 (21 de abril de 2023): 317–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-317-2023.
Texto completoVelasquez-Camacho, Luisa, Marta Otero, Boris Basile, Josep Pijuan y Giandomenico Corrado. "Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards". Microorganisms 11, n.º 1 (27 de diciembre de 2022): 73. http://dx.doi.org/10.3390/microorganisms11010073.
Texto completoAlodat, Iyas. "Analysing and predicting COVID-19 AI tracking using artificial intelligence". International Journal of Modeling, Simulation, and Scientific Computing 12, n.º 03 (17 de abril de 2021): 2141005. http://dx.doi.org/10.1142/s1793962321410051.
Texto completoHelget, Lindsay N., David J. Dillon, Bethany Wolf, Laura P. Parks, Sally E. Self, Evelyn T. Bruner, Evan E. Oates y Jim C. Oates. "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, n.º 1 (agosto de 2021): e000489. http://dx.doi.org/10.1136/lupus-2021-000489.
Texto completoChua, Felix, Rama Vancheeswaran, Adrian Draper, Tejal Vaghela, Matthew Knight, Rahul Mogal, Jaswinder Singh et al. "Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score". Thorax 76, n.º 7 (10 de marzo de 2021): 696–703. http://dx.doi.org/10.1136/thoraxjnl-2020-216425.
Texto completoMasih, Adven y 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, n.º 3 (30 de septiembre de 2020): 5–14. http://dx.doi.org/10.17308/sait.2020.3/3035.
Texto completoMrara, Busisiwe, Fathima Paruk, Constance Sewani-Rusike y 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, n.º 7 (julio de 2022): e060788. http://dx.doi.org/10.1136/bmjopen-2022-060788.
Texto completoEswaran, Sarojini, Bharathiraj L.T y 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 completoLin, Lingmin, Kailai Liu, Huan Feng, Jing Li, Hengle Chen, Tao Zhang, Boyun Xue y Jiarui Si. "Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying". Mathematical Biosciences and Engineering 19, n.º 10 (2022): 10096–107. http://dx.doi.org/10.3934/mbe.2022472.
Texto completoLiebenstund, Lisa, Mark Coburn, Christina Fitzner, Antje Willuweit, Karl-Josef Langen, Jingjin Liu, Michael Veldeman y Anke Höllig. "Predicting experimental success: a retrospective case-control study using the rat intraluminal thread model of stroke". Disease Models & Mechanisms 13, n.º 12 (22 de octubre de 2020): dmm044651. http://dx.doi.org/10.1242/dmm.044651.
Texto completoKulkarni, Mrunalini Harish, Chaitanya Kulkarni, K. Suresh Babu, Saima Ahmed Rahin, Shweta Singh y D. Dinesh Kumar. "Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT". Scientific Programming 2022 (23 de mayo de 2022): 1–10. http://dx.doi.org/10.1155/2022/3603238.
Texto completoSethy, Prabira Kumar, Santi Kumari Behera, Nithiyakanthan Kannan, Sridevi Narayanan y Chanki Pandey. "Smart paddy field monitoring system using deep learning and IoT". Concurrent Engineering 29, n.º 1 (28 de enero de 2021): 16–24. http://dx.doi.org/10.1177/1063293x21988944.
Texto completoJones, K. L., R. C. A. Thompson y S. S. Godfrey. "Social networks: a tool for assessing the impact of perturbations on wildlife behaviour and implications for pathogen transmission". Behaviour 155, n.º 7-9 (2018): 689–730. http://dx.doi.org/10.1163/1568539x-00003485.
Texto completoZhao, Hongwei, Naveed N. Merchant, Alyssa McNulty, Tiffany A. Radcliff, Murray J. Cote, Rebecca S. B. Fischer, Huiyan Sang y Marcia G. Ory. "COVID-19: Short term prediction model using daily incidence data". PLOS ONE 16, n.º 4 (14 de abril de 2021): e0250110. http://dx.doi.org/10.1371/journal.pone.0250110.
Texto completoJombart, Thibaut, Stéphane Ghozzi, Dirk Schumacher, Timothy J. Taylor, Quentin J. Leclerc, Mark Jit, Stefan Flasche 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, n.º 1829 (31 de mayo de 2021): 20200266. http://dx.doi.org/10.1098/rstb.2020.0266.
Texto completoStefanescu, Simona, Relu Cocoș, Adina Turcu-Stiolica, Elena-Silvia Shelby, Marius Matei, Mihaela-Simona Subtirelu, Andreea-Daniela Meca et al. "Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results". Pathogens 10, n.º 7 (22 de junio de 2021): 789. http://dx.doi.org/10.3390/pathogens10070789.
Texto completoANDERSON, D. P., D. S. L. RAMSEY, G. NUGENT, M. BOSSON, P. LIVINGSTONE, P. A. J. MARTIN, E. SERGEANT, A. M. GORMLEY y B. WARBURTON. "A novel approach to assess the probability of disease eradication from a wild-animal reservoir host". Epidemiology and Infection 141, n.º 7 (23 de enero de 2013): 1509–21. http://dx.doi.org/10.1017/s095026881200310x.
Texto completoPrzybilla, Jens, Peter Ahnert, Holger Bogatsch, Frank Bloos, Frank M. Brunkhorst, Michael Bauer, Markus Loeffler, Martin Witzenrath, Norbert Suttorp y Markus Scholz. "Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia". Journal of Clinical Medicine 9, n.º 2 (5 de febrero de 2020): 393. http://dx.doi.org/10.3390/jcm9020393.
Texto completoShi, Lei, Xiaoliang Feng, Longxing Qi, Yanlong Xu y Sulan Zhai. "Modeling and Predicting the Influence of PM2.5 on Children’s Respiratory Diseases". International Journal of Bifurcation and Chaos 30, n.º 15 (9 de diciembre de 2020): 2050235. http://dx.doi.org/10.1142/s0218127420502351.
Texto completoSuzuki, Ayako y Hiroshi Nishiura. "Transmission dynamics of varicella before, during and after the COVID-19 pandemic in Japan: a modelling study". Mathematical Biosciences and Engineering 19, n.º 6 (2022): 5998–6012. http://dx.doi.org/10.3934/mbe.2022280.
Texto completoSibarani, Imelda Juliana Br, Katherina Meylda Loy S y Suharjito Suharjito. "Enhancing Predictive Accuracy for Differentiated Thyroid Cancer (DTC) Recurrence Through Advanced Data Mining Techniques". TIN: Terapan Informatika Nusantara 5, n.º 1 (21 de junio de 2024): 11–22. http://dx.doi.org/10.47065/tin.v5i1.5237.
Texto completoThomas, Charlotte M., Joseph F. Standing, Catherine Smith, Satveer K. Mahil, Richard B. Warren, Jonathan Barker, Sam Norton, Zehra Arkir, Teresa Tsakok y Monica Arenas-Hernandez. "BT34 Minimizing drug exposure in psoriasis using a therapeutic drug monitoring dashboard". British Journal of Dermatology 191, Supplement_1 (28 de junio de 2024): i204—i205. http://dx.doi.org/10.1093/bjd/ljae090.431.
Texto completoFerrari, Simone, Alessandro Santus y 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 completoMaciukiewicz, M., J. Schniering, H. Gabrys, M. Brunner, C. Blüthgen, C. Meier, M. Guckenberger 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 (19 de mayo de 2021): 90. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2517.
Texto completoKantasiripitak, W., S. G. WIcha, D. Thomas, I. Hoffman, M. Ferrante, S. Vermeire, K. van Hoeve y E. Dreesen. "P531 A model-based tool for guiding infliximab induction dosing to maximise long-term deep remission in children with inflammatory bowel diseases". Journal of Crohn's and Colitis 17, Supplement_1 (30 de enero de 2023): i659—i661. http://dx.doi.org/10.1093/ecco-jcc/jjac190.0661.
Texto completoBose, Sanjukta N., Adam Verigan, Jade Hanson, Luis M. Ahumada, Sharon R. Ghazarian, Neil A. Goldenberg, Arabela Stock y Jeffrey P. Jacobs. "Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data". Cardiology in the Young 29, n.º 11 (9 de septiembre de 2019): 1340–48. http://dx.doi.org/10.1017/s1047951119002002.
Texto completoDrake, Wonder P., Connie Hsia, Lobelia Samavati, Michelle Yu, Jessica Cardenas, Fabiola G. Gianella, John Boscardin y Laura L. Koth. "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, n.º 4 (abril de 2023): e071607. http://dx.doi.org/10.1136/bmjopen-2023-071607.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoGerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022". City Healthcare 3, n.º 3 (30 de septiembre de 2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Texto completoCowled, Brendan D., Fiona Giannini, Sam D. Beckett, Andrew Woolnough, Simon Barry, Lucy Randall y Graeme Garner. "Feral pigs: predicting future distributions". Wildlife Research 36, n.º 3 (2009): 242. http://dx.doi.org/10.1071/wr08115.
Texto completoBritton, Tom y Gianpaolo Scalia Tomba. "Estimation in emerging epidemics: biases and remedies". Journal of The Royal Society Interface 16, n.º 150 (enero de 2019): 20180670. http://dx.doi.org/10.1098/rsif.2018.0670.
Texto completoGlauche, Ingmar, Hendrik Liebscher, Christoph Baldow, Matthias Kuhn, Philipp Schulze, Tom Haehnel, Astghik Voskanyan et al. "A New Computational Method to Predict Long-Term Minimal Residual Disease and Molecular Relapse after TKI-Cessation in CML". Blood 128, n.º 22 (2 de diciembre de 2016): 3099. http://dx.doi.org/10.1182/blood.v128.22.3099.3099.
Texto completoHeasley, Cole, J. Johanna Sanchez, Jordan Tustin y Ian Young. "Systematic review of predictive models of microbial water quality at freshwater recreational beaches". PLOS ONE 16, n.º 8 (26 de agosto de 2021): e0256785. http://dx.doi.org/10.1371/journal.pone.0256785.
Texto completoMarston, Christopher, Clare Rowland, Aneurin O’Neil, Seth Irish, Francis Wat’senga, Pilar Martín-Gallego, Paul Aplin, Patrick Giraudoux y Clare Strode. "Developing the Role of Earth Observation in Spatio-Temporal Mosquito Modelling to Identify Malaria Hot-Spots". Remote Sensing 15, n.º 1 (22 de diciembre de 2022): 43. http://dx.doi.org/10.3390/rs15010043.
Texto completoEjma-Multański, Adam, Anna Wajda y Agnieszka Paradowska-Gorycka. "Cell Cultures as a Versatile Tool in the Research and Treatment of Autoimmune Connective Tissue Diseases". Cells 12, n.º 20 (19 de octubre de 2023): 2489. http://dx.doi.org/10.3390/cells12202489.
Texto completoSánchez-pérez, Isabel, Jorge Melones Herrero, Alicia Villacampa, T. Sofia Figueiras, Carmela Calés, Carlos F. Sanchez Ferrer, Adoración Gómez Quiroga y Concha Peiro. "P160 MODELLING CARDIOVASCULAR TOXICITY IN CELLULO ASSOCIATED WITH ANTITUMORALS". Journal of Hypertension 42, Suppl 3 (septiembre de 2024): e119. http://dx.doi.org/10.1097/01.hjh.0001063512.42008.51.
Texto completoSkendžić, Sandra, Monika Zovko, Ivana Pajač Živković, Vinko Lešić y Darija Lemić. "The Impact of Climate Change on Agricultural Insect Pests". Insects 12, n.º 5 (12 de mayo de 2021): 440. http://dx.doi.org/10.3390/insects12050440.
Texto completoZhao, Wei, Daolun Zhang, Thomas Storme, André Baruchel, Xavier Declèves y Evelyne Jacqz-Aigrain. "POPULATION PHARMACOKINETICS AND DOSING OPTIMIZATION OF TEICOPLANIN IN CHILDREN WITH MALIGNANT HAEMATOLOGICAL DISEASE". Archives of Disease in Childhood 101, n.º 1 (14 de diciembre de 2015): e1.41-e1. http://dx.doi.org/10.1136/archdischild-2015-310148.46.
Texto completoPerera, Rafael, Richard Stevens, Jeffrey K. Aronson, Amitava Banerjee, Julie Evans, Benjamin G. Feakins, Susannah Fleming et al. "Long-term monitoring in primary care for chronic kidney disease and chronic heart failure: a multi-method research programme". Programme Grants for Applied Research 9, n.º 10 (agosto de 2021): 1–218. http://dx.doi.org/10.3310/pgfar09100.
Texto completoAkhgar, Ahmad, Dominic Sinibaldi, Lingmin Zeng, Alton B. Farris, Jason Cobb, Monica Battle, David Chain et al. "Urinary markers differentially associate with kidney inflammatory activity and chronicity measures in patients with lupus nephritis". Lupus Science & Medicine 10, n.º 1 (enero de 2023): e000747. http://dx.doi.org/10.1136/lupus-2022-000747.
Texto completoPerlini, Cinzia, Simone Garzon, Massimo Franchi, Valeria Donisi, Michela Rimondini, Mariachiara Bosco, Stefano Uccella et al. "Risk perception and affective state on work exhaustion in obstetrics during the COVID-19 pandemic". Open Medicine 17, n.º 1 (1 de enero de 2022): 1599–611. http://dx.doi.org/10.1515/med-2022-0571.
Texto completoZhang, Xianyu, Shiyao Lu, Hui Li, Xin Liu, Jun Wang, Liuhong Zeng, Zhipeng Lu et al. "Abstract P1-05-27: Liquid Biopsy for HER2 Status Assessment in Breast Cancer Using Surrogate DNA Methylation Markers". Cancer Research 83, n.º 5_Supplement (1 de marzo de 2023): P1–05–27—P1–05–27. http://dx.doi.org/10.1158/1538-7445.sabcs22-p1-05-27.
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