Artigos de revistas sobre o tema "Diabetes Complication predictions"
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Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler e Stefan Feuerriegel. "Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study". Journal of Medical Internet Research 25 (27 de fevereiro de 2023): e42181. http://dx.doi.org/10.2196/42181.
Texto completo da fonteHaber, Philipp K., Christoph Maier, Anika Kästner, Linda Feldbrügge, Santiago Andres Ortiz Galindo, Dominik Geisel, Uli Fehrenbach et al. "Predicting the Risk of Postoperative Complications in Patients Undergoing Minimally Invasive Resection of Primary Liver Tumors". Journal of Clinical Medicine 10, n.º 4 (10 de fevereiro de 2021): 685. http://dx.doi.org/10.3390/jcm10040685.
Texto completo da fonteTang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang e Jinsheng Shi. "Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records". Sensors 22, n.º 21 (3 de novembro de 2022): 8454. http://dx.doi.org/10.3390/s22218454.
Texto completo da fonteAlruwaytie, Wedad, Amal Mackawy e Ali Abu Dahash. "Cystatin C and Fibrinogen Plasma Levels as early Predictors of Diabetic Nephropathy in Type II Diabetes Mellitus; a Review Article". Pakistan Journal of Medical and Health Sciences 16, n.º 1 (30 de janeiro de 2022): 716–20. http://dx.doi.org/10.53350/pjmhs22161716.
Texto completo da fonteZuo, Ming, Wei Zhang, Qi Xu e Dehua Chen. "Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning". Journal of Healthcare Engineering 2022 (20 de abril de 2022): 1–7. http://dx.doi.org/10.1155/2022/5129125.
Texto completo da fonteLiu, Xiao-Chen, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang e Xin-Yue Li. "Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke". World Journal of Clinical Cases 12, n.º 20 (16 de julho de 2024): 4048–56. http://dx.doi.org/10.12998/wjcc.v12.i20.4048.
Texto completo da fonteChen, Xiao, Min Hou e Dongxue Wang. "Machine learning-based model for prediction of deep vein thrombosis after gynecological laparoscopy: A retrospective cohort study". Medicine 103, n.º 1 (5 de janeiro de 2024): e36717. http://dx.doi.org/10.1097/md.0000000000036717.
Texto completo da fonteHealthcare Engineering, Journal of. "Retracted: Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning". Journal of Healthcare Engineering 2023 (20 de setembro de 2023): 1. http://dx.doi.org/10.1155/2023/9891682.
Texto completo da fonteDahab, Mahmoud, Ping Zhang, Samiah Hamad Al-Mijalli e Emad M. Abdallah. "Unveiling the Anti-Cholera and Active Diabetic Renoprotective Compounds of Maqian Essential Oil: A Computational and Molecular Dynamics Study". Molecules 28, n.º 24 (5 de dezembro de 2023): 7954. http://dx.doi.org/10.3390/molecules28247954.
Texto completo da fonteAlghamdi, Turki. "Prediction of Diabetes Complications Using Computational Intelligence Techniques". Applied Sciences 13, n.º 5 (27 de fevereiro de 2023): 3030. http://dx.doi.org/10.3390/app13053030.
Texto completo da fonteYagin, Fatma Hilal, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan e Luca Paolo Ardigò. "Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy". Diagnostics 14, n.º 13 (27 de junho de 2024): 1364. http://dx.doi.org/10.3390/diagnostics14131364.
Texto completo da fonteVeerabathiran, Ramakrishnan. "Macrosomia: A Serious Complication of Diabetes in Pregnancy". Diabetes & Obesity International Journal 8, n.º 4 (2023): 1–6. http://dx.doi.org/10.23880/doij-16000280.
Texto completo da fonteAssegie, Tsehay Admassu, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan e Napa Komal Kumar. "Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier". Journal of Robotics and Control (JRC) 4, n.º 4 (4 de julho de 2023): 452–57. http://dx.doi.org/10.18196/jrc.v4i4.18412.
Texto completo da fonteBommala, Harikrishna, Kannedari Vamshi Krishna, Avusula Supriya, Rama Krishna Biradar, Bharath Mayabrahma, D. Ushasree e Evgeny Vladimirovich Kotov. "Fine-Tunining the Future: Optimizing svm hyper-parameters or enhanced diabetes prediction". MATEC Web of Conferences 392 (2024): 01082. http://dx.doi.org/10.1051/matecconf/202439201082.
Texto completo da fonteJian, Yazan, Michel Pasquier, Assim Sagahyroon e Fadi Aloul. "A Machine Learning Approach to Predicting Diabetes Complications". Healthcare 9, n.º 12 (9 de dezembro de 2021): 1712. http://dx.doi.org/10.3390/healthcare9121712.
Texto completo da fonteYousefi, Leila, e Allan Tucker. "Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction". Intelligent Data Analysis 26, n.º 2 (14 de março de 2022): 501–24. http://dx.doi.org/10.3233/ida-205570.
Texto completo da fonteBrink, Huguette S., Aart Jan van der Lely e Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes". Endocrine Connections 5, n.º 5 (setembro de 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
Texto completo da fonteV, Sathya. "Maternal Serum Biomarkers for the Early Prediction of Gestational Diabetes Mellitus". Diabetes & Obesity International Journal 4, n.º 1 (2019): 1–7. http://dx.doi.org/10.23880/doij-16000190.
Texto completo da fonteRachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak e Chayapol Kamyod. "Fuzzy based Risk Predictive Model for Cardiovascular Complication of Patient with Type 2 Diabetes Mellitus and Hypertension". ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, n.º 1 (23 de junho de 2019): 49–58. http://dx.doi.org/10.37936/ecti-cit.2019131.132114.
Texto completo da fonteHayuningtyas, Ratih Yulia, e Retno Sari. "Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes". Jurnal Teknik Komputer 8, n.º 1 (24 de janeiro de 2022): 40–44. http://dx.doi.org/10.31294/jtk.v8i1.11552.
Texto completo da fonteRozhkova, O. V., O. V. Remneva e N. V. Trukhacheva. "Prediction of perinatal complications of gestational diabetes". Fundamental and Clinical Medicine 4, n.º 4 (28 de dezembro de 2019): 19–25. http://dx.doi.org/10.23946/2500-0764-2019-4-4-19-25.
Texto completo da fonteTetreault, Lindsay, Gamaliel Tan, Branko Kopjar, Pierre Côté, Paul Arnold, Natalia Nugaeva, Giuseppe Barbagallo e Michael G. Fehlings. "Clinical and Surgical Predictors of Complications Following Surgery for the Treatment of Cervical Spondylotic Myelopathy". Neurosurgery 79, n.º 1 (25 de novembro de 2015): 33–44. http://dx.doi.org/10.1227/neu.0000000000001151.
Texto completo da fonteKathiravan A., Dr T. Ananth kumar e Dr P. Kanimozhi. "A Survey on Implementing Machine Learning Algorithm to Predict Diabetes Stages and Preventing Elevated Blood Glucose Levels". Irish Interdisciplinary Journal of Science & Research 07, n.º 04 (2023): 18–24. http://dx.doi.org/10.46759/iijsr.2023.7403.
Texto completo da fonteSong, Xing, Lemuel Russ Waitman, Alan SL Yu, David C. Robbins, Yong Hu e Mei Liu. "Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients using Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study". JMIR Medical Informatics 8, n.º 1 (24 de janeiro de 2020): e15510. http://dx.doi.org/10.2196/15510.
Texto completo da fonteKumari, Gorli L. Aruna, Poosapati Padmaja e Jaya G. Suma. "A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory". Indonesian Journal of Electrical Engineering and Computer Science 26, n.º 1 (1 de abril de 2022): 404. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp404-413.
Texto completo da fonteKartina Diah Kusuma Wardani e Memen Akbar. "Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost)". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, n.º 4 (12 de agosto de 2023): 824–31. http://dx.doi.org/10.29207/resti.v7i4.4651.
Texto completo da fonteGardner, Clarissa, Deborah Wake, Doogie Brodie, Alex Silverstein, Sophie Young, Scott Cunningham, Chris Sainsbury et al. "Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method". DIGITAL HEALTH 9 (janeiro de 2023): 205520762211286. http://dx.doi.org/10.1177/20552076221128677.
Texto completo da fonteSaxena, Roshi, Sanjay Kumar Sharma, Manali Gupta e G. C. Sampada. "A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey". Journal of Healthcare Engineering 2022 (12 de abril de 2022): 1–15. http://dx.doi.org/10.1155/2022/8100697.
Texto completo da fonteEl-Sofany, Hosam, Samir A. El-Seoud, Omar H. Karam, Yasser M. Abd El-Latif e Islam A. T. F. Taj-Eddin. "A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App". International Journal of Intelligent Systems 2024 (9 de janeiro de 2024): 1–13. http://dx.doi.org/10.1155/2024/6688934.
Texto completo da fonteAlmheiri, Ali, Amna Alhammadi, Fatima AlShehhi, Asma Mohammad, Rodha Alshamsi, Khaled Alzaman, Saima Jabeen e Burhan Haq. "Biomarkers for Prediabetes, Type 2 Diabetes, and Associated Complications". American Journal of Health, Medicine and Nursing Practice 9, n.º 2 (27 de setembro de 2023): 1–21. http://dx.doi.org/10.47672/ajhmn.1592.
Texto completo da fonteRasha Rokan Ismail. "Early diagnosing diabetes using data mining algorithms". Global Journal of Engineering and Technology Advances 16, n.º 2 (30 de agosto de 2023): 106–13. http://dx.doi.org/10.30574/gjeta.2023.16.2.0141.
Texto completo da fonteGinting, Rapael Ginting, Ermi Girsang, Johannes Bastira Ginting e Hartono Hartono. "ANALISIS DETERMINAN DAN PREDIKSI PENYAKIT DIABETES MELITUS TIPE 2 MENGGUNAKAN METODE MACHINE LEARNING: SCOPING REVIEW". Jurnal Maternitas Kebidanan 7, n.º 1 (16 de abril de 2022): 58–72. http://dx.doi.org/10.34012/jumkep.v7i1.2538.
Texto completo da fonteZiajor, Seweryn, Justyna Tomasik, Piotr Sajdak, Mikołaj Turski, Artur Bednarski, Marcel Stodolak, Łukasz Szydłowski et al. "The use of artificial intelligence in the diagnosis and detection of complications of diabetes". Journal of Education, Health and Sport 65 (11 de abril de 2024): 11–27. http://dx.doi.org/10.12775/jehs.2024.65.001.
Texto completo da fonteAgliata, Antonio, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano e Roberto Tagliaferri. "Machine Learning as a Support for the Diagnosis of Type 2 Diabetes". International Journal of Molecular Sciences 24, n.º 7 (5 de abril de 2023): 6775. http://dx.doi.org/10.3390/ijms24076775.
Texto completo da fonteNijpels, Giel, Joline WJ Beulens, Amber AWA van der Heijden e Petra J. Elders. "Innovations in personalised diabetes care and risk management". European Journal of Preventive Cardiology 26, n.º 2_suppl (26 de novembro de 2019): 125–32. http://dx.doi.org/10.1177/2047487319880043.
Texto completo da fonteTanabe, Hayato, Haruka Saito, Akihiro Kudo, Noritaka Machii, Hiroyuki Hirai, Gulinu Maimaituxun, Kenichi Tanaka et al. "Factors Associated with Risk of Diabetic Complications in Novel Cluster-Based Diabetes Subgroups: A Japanese Retrospective Cohort Study". Journal of Clinical Medicine 9, n.º 7 (2 de julho de 2020): 2083. http://dx.doi.org/10.3390/jcm9072083.
Texto completo da fonteLu, Huiqi Y., Ping Lu, Jane E. Hirst, Lucy Mackillop e David A. Clifton. "A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus". Sensors 23, n.º 18 (20 de setembro de 2023): 7990. http://dx.doi.org/10.3390/s23187990.
Texto completo da fonteLuo, Xin, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao e An Zhang. "Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining". PLOS ONE 18, n.º 8 (8 de agosto de 2023): e0289749. http://dx.doi.org/10.1371/journal.pone.0289749.
Texto completo da fonteVijayan, Midhula, e Venkatakrishnan S. "A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet". Diagnostics 13, n.º 4 (17 de fevereiro de 2023): 774. http://dx.doi.org/10.3390/diagnostics13040774.
Texto completo da fonteLin, Ming-Yen, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang e Hsing Luh. "Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases". Information 12, n.º 8 (12 de agosto de 2021): 326. http://dx.doi.org/10.3390/info12080326.
Texto completo da fonteCEVHER AKDULUM, Munire Funda, Erhan DEMİRDAĞ, Safarova SAHİLA, Mehmet ERDEM e Ahmet ERDEM. "İlk Trimesterde Sistemik İmmün-İnflamasyon İndeksini Kullanarak Gestasyonel Diabetes Mellitus'u Tahmin Etme". Journal of Contemporary Medicine 12, n.º 5 (30 de setembro de 2022): 617–20. http://dx.doi.org/10.16899/jcm.1148179.
Texto completo da fonteAbdalrada, Ahmad Shaker, Jemal Abawajy, Tahsien Al-Quraishi e Sheikh Mohammed Shariful Islam. "Prediction of cardiac autonomic neuropathy using a machine learning model in patients with diabetes". Therapeutic Advances in Endocrinology and Metabolism 13 (janeiro de 2022): 204201882210866. http://dx.doi.org/10.1177/20420188221086693.
Texto completo da fonteFebriani, Irene. "Undiagnosed Diabetes Prediction With Development of Scoring System Based on Risk Factors". Preventif : Jurnal Kesehatan Masyarakat 11, n.º 1 (3 de agosto de 2020): 9–21. http://dx.doi.org/10.22487/preventif.v11i1.54.
Texto completo da fonteJose, Rejath, Faiz Syed, Anvin Thomas e Milan Toma. "Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions". Applied Sciences 14, n.º 5 (4 de março de 2024): 2132. http://dx.doi.org/10.3390/app14052132.
Texto completo da fonteHan, Yiteng, Qixuan Li, Jinghui Lou e Jingrui Zhang. "Prediction of diabetes progress based on machine learning approach". Applied and Computational Engineering 37, n.º 1 (22 de janeiro de 2024): 45–51. http://dx.doi.org/10.54254/2755-2721/37/20230468.
Texto completo da fonteZhan, Wenqiang, Jing Zhu, Xiaolin Hua, Jiangfeng Ye, Qian Chen e Jun Zhang. "Epidemiology of uterine rupture among pregnant women in China and development of a risk prediction model: analysis of data from a multicentre, cross-sectional study". BMJ Open 11, n.º 11 (novembro de 2021): e054540. http://dx.doi.org/10.1136/bmjopen-2021-054540.
Texto completo da fonteWan, Eric Yuk Fai, Esther Yee Tak Yu, Weng Yee Chin, Colman Siu Cheung Fung, Ruby Lai Ping Kwok, David Vai Kiong Chao, King Hong Chan et al. "Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol". BMJ Open 8, n.º 10 (outubro de 2018): e023070. http://dx.doi.org/10.1136/bmjopen-2018-023070.
Texto completo da fonteNdjaboue, Ruth, Gérard Ngueta, Charlotte Rochefort-Brihay, Daniel Guay, Sasha Delorme, Noah Ivers, Baiju Shah et al. "Risk Prediction Models of Diabetes Complications: A Scoping Review". Canadian Journal of Diabetes 45, n.º 7 (novembro de 2021): S32. http://dx.doi.org/10.1016/j.jcjd.2021.09.095.
Texto completo da fonteQian, Dongni, e Hong Gao. "Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression". Computational and Mathematical Methods in Medicine 2022 (29 de julho de 2022): 1–7. http://dx.doi.org/10.1155/2022/3882425.
Texto completo da fonteHD, Sowmya, Shreyaskar sanskar, Pawan tiwari e Kishan kumar. "Diabetes Prediction Using Machine Learning Algorithm". International Journal of Innovative Research in Information Security 09, n.º 03 (23 de junho de 2023): 115–20. http://dx.doi.org/10.26562/ijiris.2023.v0903.14.
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