Zeitschriftenartikel zum Thema „Diabetes Complication predictions“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Diabetes Complication predictions" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler und 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 (27.02.2023): e42181. http://dx.doi.org/10.2196/42181.
Der volle Inhalt der QuelleHaber, 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, Nr. 4 (10.02.2021): 685. http://dx.doi.org/10.3390/jcm10040685.
Der volle Inhalt der QuelleTang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang und Jinsheng Shi. „Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records“. Sensors 22, Nr. 21 (03.11.2022): 8454. http://dx.doi.org/10.3390/s22218454.
Der volle Inhalt der QuelleAlruwaytie, Wedad, Amal Mackawy und 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, Nr. 1 (30.01.2022): 716–20. http://dx.doi.org/10.53350/pjmhs22161716.
Der volle Inhalt der QuelleZuo, Ming, Wei Zhang, Qi Xu und Dehua Chen. „Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning“. Journal of Healthcare Engineering 2022 (20.04.2022): 1–7. http://dx.doi.org/10.1155/2022/5129125.
Der volle Inhalt der QuelleLiu, Xiao-Chen, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang und 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, Nr. 20 (16.07.2024): 4048–56. http://dx.doi.org/10.12998/wjcc.v12.i20.4048.
Der volle Inhalt der QuelleChen, Xiao, Min Hou und Dongxue Wang. „Machine learning-based model for prediction of deep vein thrombosis after gynecological laparoscopy: A retrospective cohort study“. Medicine 103, Nr. 1 (05.01.2024): e36717. http://dx.doi.org/10.1097/md.0000000000036717.
Der volle Inhalt der QuelleHealthcare 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 (20.09.2023): 1. http://dx.doi.org/10.1155/2023/9891682.
Der volle Inhalt der QuelleDahab, Mahmoud, Ping Zhang, Samiah Hamad Al-Mijalli und Emad M. Abdallah. „Unveiling the Anti-Cholera and Active Diabetic Renoprotective Compounds of Maqian Essential Oil: A Computational and Molecular Dynamics Study“. Molecules 28, Nr. 24 (05.12.2023): 7954. http://dx.doi.org/10.3390/molecules28247954.
Der volle Inhalt der QuelleAlghamdi, Turki. „Prediction of Diabetes Complications Using Computational Intelligence Techniques“. Applied Sciences 13, Nr. 5 (27.02.2023): 3030. http://dx.doi.org/10.3390/app13053030.
Der volle Inhalt der QuelleYagin, Fatma Hilal, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan und Luca Paolo Ardigò. „Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy“. Diagnostics 14, Nr. 13 (27.06.2024): 1364. http://dx.doi.org/10.3390/diagnostics14131364.
Der volle Inhalt der QuelleVeerabathiran, Ramakrishnan. „Macrosomia: A Serious Complication of Diabetes in Pregnancy“. Diabetes & Obesity International Journal 8, Nr. 4 (2023): 1–6. http://dx.doi.org/10.23880/doij-16000280.
Der volle Inhalt der QuelleAssegie, Tsehay Admassu, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan und Napa Komal Kumar. „Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier“. Journal of Robotics and Control (JRC) 4, Nr. 4 (04.07.2023): 452–57. http://dx.doi.org/10.18196/jrc.v4i4.18412.
Der volle Inhalt der QuelleBommala, Harikrishna, Kannedari Vamshi Krishna, Avusula Supriya, Rama Krishna Biradar, Bharath Mayabrahma, D. Ushasree und 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.
Der volle Inhalt der QuelleJian, Yazan, Michel Pasquier, Assim Sagahyroon und Fadi Aloul. „A Machine Learning Approach to Predicting Diabetes Complications“. Healthcare 9, Nr. 12 (09.12.2021): 1712. http://dx.doi.org/10.3390/healthcare9121712.
Der volle Inhalt der QuelleYousefi, Leila, und Allan Tucker. „Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction“. Intelligent Data Analysis 26, Nr. 2 (14.03.2022): 501–24. http://dx.doi.org/10.3233/ida-205570.
Der volle Inhalt der QuelleBrink, Huguette S., Aart Jan van der Lely und Joke van der Linden. „The potential role of biomarkers in predicting gestational diabetes“. Endocrine Connections 5, Nr. 5 (September 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
Der volle Inhalt der QuelleV, Sathya. „Maternal Serum Biomarkers for the Early Prediction of Gestational Diabetes Mellitus“. Diabetes & Obesity International Journal 4, Nr. 1 (2019): 1–7. http://dx.doi.org/10.23880/doij-16000190.
Der volle Inhalt der QuelleRachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak und 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, Nr. 1 (23.06.2019): 49–58. http://dx.doi.org/10.37936/ecti-cit.2019131.132114.
Der volle Inhalt der QuelleHayuningtyas, Ratih Yulia, und Retno Sari. „Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes“. Jurnal Teknik Komputer 8, Nr. 1 (24.01.2022): 40–44. http://dx.doi.org/10.31294/jtk.v8i1.11552.
Der volle Inhalt der QuelleRozhkova, O. V., O. V. Remneva und N. V. Trukhacheva. „Prediction of perinatal complications of gestational diabetes“. Fundamental and Clinical Medicine 4, Nr. 4 (28.12.2019): 19–25. http://dx.doi.org/10.23946/2500-0764-2019-4-4-19-25.
Der volle Inhalt der QuelleTetreault, Lindsay, Gamaliel Tan, Branko Kopjar, Pierre Côté, Paul Arnold, Natalia Nugaeva, Giuseppe Barbagallo und Michael G. Fehlings. „Clinical and Surgical Predictors of Complications Following Surgery for the Treatment of Cervical Spondylotic Myelopathy“. Neurosurgery 79, Nr. 1 (25.11.2015): 33–44. http://dx.doi.org/10.1227/neu.0000000000001151.
Der volle Inhalt der QuelleKathiravan A., Dr T. Ananth kumar und 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, Nr. 04 (2023): 18–24. http://dx.doi.org/10.46759/iijsr.2023.7403.
Der volle Inhalt der QuelleSong, Xing, Lemuel Russ Waitman, Alan SL Yu, David C. Robbins, Yong Hu und 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, Nr. 1 (24.01.2020): e15510. http://dx.doi.org/10.2196/15510.
Der volle Inhalt der QuelleKumari, Gorli L. Aruna, Poosapati Padmaja und 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, Nr. 1 (01.04.2022): 404. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp404-413.
Der volle Inhalt der QuelleKartina Diah Kusuma Wardani und Memen Akbar. „Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost)“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, Nr. 4 (12.08.2023): 824–31. http://dx.doi.org/10.29207/resti.v7i4.4651.
Der volle Inhalt der QuelleGardner, 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 (Januar 2023): 205520762211286. http://dx.doi.org/10.1177/20552076221128677.
Der volle Inhalt der QuelleSaxena, Roshi, Sanjay Kumar Sharma, Manali Gupta und G. C. Sampada. „A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey“. Journal of Healthcare Engineering 2022 (12.04.2022): 1–15. http://dx.doi.org/10.1155/2022/8100697.
Der volle Inhalt der QuelleEl-Sofany, Hosam, Samir A. El-Seoud, Omar H. Karam, Yasser M. Abd El-Latif und 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 (09.01.2024): 1–13. http://dx.doi.org/10.1155/2024/6688934.
Der volle Inhalt der QuelleAlmheiri, Ali, Amna Alhammadi, Fatima AlShehhi, Asma Mohammad, Rodha Alshamsi, Khaled Alzaman, Saima Jabeen und Burhan Haq. „Biomarkers for Prediabetes, Type 2 Diabetes, and Associated Complications“. American Journal of Health, Medicine and Nursing Practice 9, Nr. 2 (27.09.2023): 1–21. http://dx.doi.org/10.47672/ajhmn.1592.
Der volle Inhalt der QuelleRasha Rokan Ismail. „Early diagnosing diabetes using data mining algorithms“. Global Journal of Engineering and Technology Advances 16, Nr. 2 (30.08.2023): 106–13. http://dx.doi.org/10.30574/gjeta.2023.16.2.0141.
Der volle Inhalt der QuelleGinting, Rapael Ginting, Ermi Girsang, Johannes Bastira Ginting und Hartono Hartono. „ANALISIS DETERMINAN DAN PREDIKSI PENYAKIT DIABETES MELITUS TIPE 2 MENGGUNAKAN METODE MACHINE LEARNING: SCOPING REVIEW“. Jurnal Maternitas Kebidanan 7, Nr. 1 (16.04.2022): 58–72. http://dx.doi.org/10.34012/jumkep.v7i1.2538.
Der volle Inhalt der QuelleZiajor, 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 (11.04.2024): 11–27. http://dx.doi.org/10.12775/jehs.2024.65.001.
Der volle Inhalt der QuelleAgliata, Antonio, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano und Roberto Tagliaferri. „Machine Learning as a Support for the Diagnosis of Type 2 Diabetes“. International Journal of Molecular Sciences 24, Nr. 7 (05.04.2023): 6775. http://dx.doi.org/10.3390/ijms24076775.
Der volle Inhalt der QuelleNijpels, Giel, Joline WJ Beulens, Amber AWA van der Heijden und Petra J. Elders. „Innovations in personalised diabetes care and risk management“. European Journal of Preventive Cardiology 26, Nr. 2_suppl (26.11.2019): 125–32. http://dx.doi.org/10.1177/2047487319880043.
Der volle Inhalt der QuelleTanabe, 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, Nr. 7 (02.07.2020): 2083. http://dx.doi.org/10.3390/jcm9072083.
Der volle Inhalt der QuelleLu, Huiqi Y., Ping Lu, Jane E. Hirst, Lucy Mackillop und David A. Clifton. „A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus“. Sensors 23, Nr. 18 (20.09.2023): 7990. http://dx.doi.org/10.3390/s23187990.
Der volle Inhalt der QuelleLuo, Xin, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao und 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, Nr. 8 (08.08.2023): e0289749. http://dx.doi.org/10.1371/journal.pone.0289749.
Der volle Inhalt der QuelleVijayan, Midhula, und Venkatakrishnan S. „A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet“. Diagnostics 13, Nr. 4 (17.02.2023): 774. http://dx.doi.org/10.3390/diagnostics13040774.
Der volle Inhalt der QuelleLin, Ming-Yen, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang und 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, Nr. 8 (12.08.2021): 326. http://dx.doi.org/10.3390/info12080326.
Der volle Inhalt der QuelleCEVHER AKDULUM, Munire Funda, Erhan DEMİRDAĞ, Safarova SAHİLA, Mehmet ERDEM und Ahmet ERDEM. „İlk Trimesterde Sistemik İmmün-İnflamasyon İndeksini Kullanarak Gestasyonel Diabetes Mellitus'u Tahmin Etme“. Journal of Contemporary Medicine 12, Nr. 5 (30.09.2022): 617–20. http://dx.doi.org/10.16899/jcm.1148179.
Der volle Inhalt der QuelleAbdalrada, Ahmad Shaker, Jemal Abawajy, Tahsien Al-Quraishi und 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 (Januar 2022): 204201882210866. http://dx.doi.org/10.1177/20420188221086693.
Der volle Inhalt der QuelleFebriani, Irene. „Undiagnosed Diabetes Prediction With Development of Scoring System Based on Risk Factors“. Preventif : Jurnal Kesehatan Masyarakat 11, Nr. 1 (03.08.2020): 9–21. http://dx.doi.org/10.22487/preventif.v11i1.54.
Der volle Inhalt der QuelleJose, Rejath, Faiz Syed, Anvin Thomas und Milan Toma. „Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions“. Applied Sciences 14, Nr. 5 (04.03.2024): 2132. http://dx.doi.org/10.3390/app14052132.
Der volle Inhalt der QuelleHan, Yiteng, Qixuan Li, Jinghui Lou und Jingrui Zhang. „Prediction of diabetes progress based on machine learning approach“. Applied and Computational Engineering 37, Nr. 1 (22.01.2024): 45–51. http://dx.doi.org/10.54254/2755-2721/37/20230468.
Der volle Inhalt der QuelleZhan, Wenqiang, Jing Zhu, Xiaolin Hua, Jiangfeng Ye, Qian Chen und 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, Nr. 11 (November 2021): e054540. http://dx.doi.org/10.1136/bmjopen-2021-054540.
Der volle Inhalt der QuelleWan, 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, Nr. 10 (Oktober 2018): e023070. http://dx.doi.org/10.1136/bmjopen-2018-023070.
Der volle Inhalt der QuelleNdjaboue, 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, Nr. 7 (November 2021): S32. http://dx.doi.org/10.1016/j.jcjd.2021.09.095.
Der volle Inhalt der QuelleQian, Dongni, und 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 (29.07.2022): 1–7. http://dx.doi.org/10.1155/2022/3882425.
Der volle Inhalt der QuelleHD, Sowmya, Shreyaskar sanskar, Pawan tiwari und Kishan kumar. „Diabetes Prediction Using Machine Learning Algorithm“. International Journal of Innovative Research in Information Security 09, Nr. 03 (23.06.2023): 115–20. http://dx.doi.org/10.26562/ijiris.2023.v0903.14.
Der volle Inhalt der Quelle