Artículos de revistas sobre el tema "Diabetes Complication predictions"
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Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler y 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 febrero de 2023): e42181. http://dx.doi.org/10.2196/42181.
Texto completoHaber, 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 febrero de 2021): 685. http://dx.doi.org/10.3390/jcm10040685.
Texto completoTang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang y Jinsheng Shi. "Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records". Sensors 22, n.º 21 (3 de noviembre de 2022): 8454. http://dx.doi.org/10.3390/s22218454.
Texto completoAlruwaytie, Wedad, Amal Mackawy y 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 enero de 2022): 716–20. http://dx.doi.org/10.53350/pjmhs22161716.
Texto completoZuo, Ming, Wei Zhang, Qi Xu y 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 completoLiu, Xiao-Chen, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang y 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 julio de 2024): 4048–56. http://dx.doi.org/10.12998/wjcc.v12.i20.4048.
Texto completoChen, Xiao, Min Hou y 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 enero de 2024): e36717. http://dx.doi.org/10.1097/md.0000000000036717.
Texto completoHealthcare 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 septiembre de 2023): 1. http://dx.doi.org/10.1155/2023/9891682.
Texto completoDahab, Mahmoud, Ping Zhang, Samiah Hamad Al-Mijalli y 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 diciembre de 2023): 7954. http://dx.doi.org/10.3390/molecules28247954.
Texto completoAlghamdi, Turki. "Prediction of Diabetes Complications Using Computational Intelligence Techniques". Applied Sciences 13, n.º 5 (27 de febrero de 2023): 3030. http://dx.doi.org/10.3390/app13053030.
Texto completoYagin, Fatma Hilal, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan y Luca Paolo Ardigò. "Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy". Diagnostics 14, n.º 13 (27 de junio de 2024): 1364. http://dx.doi.org/10.3390/diagnostics14131364.
Texto completoVeerabathiran, 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 completoAssegie, Tsehay Admassu, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan y 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 julio de 2023): 452–57. http://dx.doi.org/10.18196/jrc.v4i4.18412.
Texto completoBommala, Harikrishna, Kannedari Vamshi Krishna, Avusula Supriya, Rama Krishna Biradar, Bharath Mayabrahma, D. Ushasree y 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 completoJian, Yazan, Michel Pasquier, Assim Sagahyroon y Fadi Aloul. "A Machine Learning Approach to Predicting Diabetes Complications". Healthcare 9, n.º 12 (9 de diciembre de 2021): 1712. http://dx.doi.org/10.3390/healthcare9121712.
Texto completoYousefi, Leila y 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 marzo de 2022): 501–24. http://dx.doi.org/10.3233/ida-205570.
Texto completoBrink, Huguette S., Aart Jan van der Lely y Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes". Endocrine Connections 5, n.º 5 (septiembre de 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
Texto completoV, 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 completoRachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak y 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 junio de 2019): 49–58. http://dx.doi.org/10.37936/ecti-cit.2019131.132114.
Texto completoHayuningtyas, Ratih Yulia y Retno Sari. "Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes". Jurnal Teknik Komputer 8, n.º 1 (24 de enero de 2022): 40–44. http://dx.doi.org/10.31294/jtk.v8i1.11552.
Texto completoRozhkova, O. V., O. V. Remneva y N. V. Trukhacheva. "Prediction of perinatal complications of gestational diabetes". Fundamental and Clinical Medicine 4, n.º 4 (28 de diciembre de 2019): 19–25. http://dx.doi.org/10.23946/2500-0764-2019-4-4-19-25.
Texto completoTetreault, Lindsay, Gamaliel Tan, Branko Kopjar, Pierre Côté, Paul Arnold, Natalia Nugaeva, Giuseppe Barbagallo y 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 noviembre de 2015): 33–44. http://dx.doi.org/10.1227/neu.0000000000001151.
Texto completoKathiravan A., Dr T. Ananth kumar y 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 completoSong, Xing, Lemuel Russ Waitman, Alan SL Yu, David C. Robbins, Yong Hu y 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 enero de 2020): e15510. http://dx.doi.org/10.2196/15510.
Texto completoKumari, Gorli L. Aruna, Poosapati Padmaja y 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 completoKartina Diah Kusuma Wardani y 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 completoGardner, 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 (enero de 2023): 205520762211286. http://dx.doi.org/10.1177/20552076221128677.
Texto completoSaxena, Roshi, Sanjay Kumar Sharma, Manali Gupta y 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 completoEl-Sofany, Hosam, Samir A. El-Seoud, Omar H. Karam, Yasser M. Abd El-Latif y 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 enero de 2024): 1–13. http://dx.doi.org/10.1155/2024/6688934.
Texto completoAlmheiri, Ali, Amna Alhammadi, Fatima AlShehhi, Asma Mohammad, Rodha Alshamsi, Khaled Alzaman, Saima Jabeen y 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 septiembre de 2023): 1–21. http://dx.doi.org/10.47672/ajhmn.1592.
Texto completoRasha 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 completoGinting, Rapael Ginting, Ermi Girsang, Johannes Bastira Ginting y 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 completoZiajor, 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 completoAgliata, Antonio, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano y 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 completoNijpels, Giel, Joline WJ Beulens, Amber AWA van der Heijden y Petra J. Elders. "Innovations in personalised diabetes care and risk management". European Journal of Preventive Cardiology 26, n.º 2_suppl (26 de noviembre de 2019): 125–32. http://dx.doi.org/10.1177/2047487319880043.
Texto completoTanabe, 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 julio de 2020): 2083. http://dx.doi.org/10.3390/jcm9072083.
Texto completoLu, Huiqi Y., Ping Lu, Jane E. Hirst, Lucy Mackillop y 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 septiembre de 2023): 7990. http://dx.doi.org/10.3390/s23187990.
Texto completoLuo, Xin, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao y 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 completoVijayan, Midhula y Venkatakrishnan S. "A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet". Diagnostics 13, n.º 4 (17 de febrero de 2023): 774. http://dx.doi.org/10.3390/diagnostics13040774.
Texto completoLin, Ming-Yen, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang y 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 completoCEVHER AKDULUM, Munire Funda, Erhan DEMİRDAĞ, Safarova SAHİLA, Mehmet ERDEM y 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 septiembre de 2022): 617–20. http://dx.doi.org/10.16899/jcm.1148179.
Texto completoAbdalrada, Ahmad Shaker, Jemal Abawajy, Tahsien Al-Quraishi y 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 (enero de 2022): 204201882210866. http://dx.doi.org/10.1177/20420188221086693.
Texto completoFebriani, 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 completoJose, Rejath, Faiz Syed, Anvin Thomas y Milan Toma. "Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions". Applied Sciences 14, n.º 5 (4 de marzo de 2024): 2132. http://dx.doi.org/10.3390/app14052132.
Texto completoHan, Yiteng, Qixuan Li, Jinghui Lou y Jingrui Zhang. "Prediction of diabetes progress based on machine learning approach". Applied and Computational Engineering 37, n.º 1 (22 de enero de 2024): 45–51. http://dx.doi.org/10.54254/2755-2721/37/20230468.
Texto completoZhan, Wenqiang, Jing Zhu, Xiaolin Hua, Jiangfeng Ye, Qian Chen y 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 (noviembre de 2021): e054540. http://dx.doi.org/10.1136/bmjopen-2021-054540.
Texto completoWan, 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 (octubre de 2018): e023070. http://dx.doi.org/10.1136/bmjopen-2018-023070.
Texto completoNdjaboue, 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 (noviembre de 2021): S32. http://dx.doi.org/10.1016/j.jcjd.2021.09.095.
Texto completoQian, Dongni y 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 julio de 2022): 1–7. http://dx.doi.org/10.1155/2022/3882425.
Texto completoHD, Sowmya, Shreyaskar sanskar, Pawan tiwari y Kishan kumar. "Diabetes Prediction Using Machine Learning Algorithm". International Journal of Innovative Research in Information Security 09, n.º 03 (23 de junio de 2023): 115–20. http://dx.doi.org/10.26562/ijiris.2023.v0903.14.
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