Artykuły w czasopismach na temat „Diabetes Complication predictions”
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Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler i 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.
Pełny tekst źródłaHaber, Philipp K., Christoph Maier, Anika Kästner, Linda Feldbrügge, Santiago Andres Ortiz Galindo, Dominik Geisel, Uli Fehrenbach i in. "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.
Pełny tekst źródłaTang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang i Jinsheng Shi. "Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records". Sensors 22, nr 21 (3.11.2022): 8454. http://dx.doi.org/10.3390/s22218454.
Pełny tekst źródłaAlruwaytie, Wedad, Amal Mackawy i 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.
Pełny tekst źródłaZuo, Ming, Wei Zhang, Qi Xu i 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.
Pełny tekst źródłaLiu, Xiao-Chen, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang i 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.
Pełny tekst źródłaChen, Xiao, Min Hou i Dongxue Wang. "Machine learning-based model for prediction of deep vein thrombosis after gynecological laparoscopy: A retrospective cohort study". Medicine 103, nr 1 (5.01.2024): e36717. http://dx.doi.org/10.1097/md.0000000000036717.
Pełny tekst źródłaHealthcare 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.
Pełny tekst źródłaDahab, Mahmoud, Ping Zhang, Samiah Hamad Al-Mijalli i 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 (5.12.2023): 7954. http://dx.doi.org/10.3390/molecules28247954.
Pełny tekst źródłaAlghamdi, 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.
Pełny tekst źródłaYagin, Fatma Hilal, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan i 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.
Pełny tekst źródłaVeerabathiran, 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.
Pełny tekst źródłaAssegie, Tsehay Admassu, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan i Napa Komal Kumar. "Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier". Journal of Robotics and Control (JRC) 4, nr 4 (4.07.2023): 452–57. http://dx.doi.org/10.18196/jrc.v4i4.18412.
Pełny tekst źródłaBommala, Harikrishna, Kannedari Vamshi Krishna, Avusula Supriya, Rama Krishna Biradar, Bharath Mayabrahma, D. Ushasree i 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.
Pełny tekst źródłaJian, Yazan, Michel Pasquier, Assim Sagahyroon i Fadi Aloul. "A Machine Learning Approach to Predicting Diabetes Complications". Healthcare 9, nr 12 (9.12.2021): 1712. http://dx.doi.org/10.3390/healthcare9121712.
Pełny tekst źródłaYousefi, Leila, i 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.
Pełny tekst źródłaBrink, Huguette S., Aart Jan van der Lely i Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes". Endocrine Connections 5, nr 5 (wrzesień 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
Pełny tekst źródłaV, 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.
Pełny tekst źródłaRachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak i 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.
Pełny tekst źródłaHayuningtyas, Ratih Yulia, i 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.
Pełny tekst źródłaRozhkova, O. V., O. V. Remneva i 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.
Pełny tekst źródłaTetreault, Lindsay, Gamaliel Tan, Branko Kopjar, Pierre Côté, Paul Arnold, Natalia Nugaeva, Giuseppe Barbagallo i 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.
Pełny tekst źródłaKathiravan A., Dr T. Ananth kumar i 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.
Pełny tekst źródłaSong, Xing, Lemuel Russ Waitman, Alan SL Yu, David C. Robbins, Yong Hu i 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.
Pełny tekst źródłaKumari, Gorli L. Aruna, Poosapati Padmaja i 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 (1.04.2022): 404. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp404-413.
Pełny tekst źródłaKartina Diah Kusuma Wardani i 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.
Pełny tekst źródłaGardner, Clarissa, Deborah Wake, Doogie Brodie, Alex Silverstein, Sophie Young, Scott Cunningham, Chris Sainsbury i in. "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 (styczeń 2023): 205520762211286. http://dx.doi.org/10.1177/20552076221128677.
Pełny tekst źródłaSaxena, Roshi, Sanjay Kumar Sharma, Manali Gupta i 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.
Pełny tekst źródłaEl-Sofany, Hosam, Samir A. El-Seoud, Omar H. Karam, Yasser M. Abd El-Latif i 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 (9.01.2024): 1–13. http://dx.doi.org/10.1155/2024/6688934.
Pełny tekst źródłaAlmheiri, Ali, Amna Alhammadi, Fatima AlShehhi, Asma Mohammad, Rodha Alshamsi, Khaled Alzaman, Saima Jabeen i 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.
Pełny tekst źródłaRasha 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.
Pełny tekst źródłaGinting, Rapael Ginting, Ermi Girsang, Johannes Bastira Ginting i 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.
Pełny tekst źródłaZiajor, Seweryn, Justyna Tomasik, Piotr Sajdak, Mikołaj Turski, Artur Bednarski, Marcel Stodolak, Łukasz Szydłowski i in. "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.
Pełny tekst źródłaAgliata, Antonio, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano i Roberto Tagliaferri. "Machine Learning as a Support for the Diagnosis of Type 2 Diabetes". International Journal of Molecular Sciences 24, nr 7 (5.04.2023): 6775. http://dx.doi.org/10.3390/ijms24076775.
Pełny tekst źródłaNijpels, Giel, Joline WJ Beulens, Amber AWA van der Heijden i 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.
Pełny tekst źródłaTanabe, Hayato, Haruka Saito, Akihiro Kudo, Noritaka Machii, Hiroyuki Hirai, Gulinu Maimaituxun, Kenichi Tanaka i in. "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 (2.07.2020): 2083. http://dx.doi.org/10.3390/jcm9072083.
Pełny tekst źródłaLu, Huiqi Y., Ping Lu, Jane E. Hirst, Lucy Mackillop i 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.
Pełny tekst źródłaLuo, Xin, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao i 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 (8.08.2023): e0289749. http://dx.doi.org/10.1371/journal.pone.0289749.
Pełny tekst źródłaVijayan, Midhula, i 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.
Pełny tekst źródłaLin, Ming-Yen, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang i 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.
Pełny tekst źródłaCEVHER AKDULUM, Munire Funda, Erhan DEMİRDAĞ, Safarova SAHİLA, Mehmet ERDEM i 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.
Pełny tekst źródłaAbdalrada, Ahmad Shaker, Jemal Abawajy, Tahsien Al-Quraishi i 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 (styczeń 2022): 204201882210866. http://dx.doi.org/10.1177/20420188221086693.
Pełny tekst źródłaFebriani, Irene. "Undiagnosed Diabetes Prediction With Development of Scoring System Based on Risk Factors". Preventif : Jurnal Kesehatan Masyarakat 11, nr 1 (3.08.2020): 9–21. http://dx.doi.org/10.22487/preventif.v11i1.54.
Pełny tekst źródłaJose, Rejath, Faiz Syed, Anvin Thomas i Milan Toma. "Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions". Applied Sciences 14, nr 5 (4.03.2024): 2132. http://dx.doi.org/10.3390/app14052132.
Pełny tekst źródłaHan, Yiteng, Qixuan Li, Jinghui Lou i 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.
Pełny tekst źródłaZhan, Wenqiang, Jing Zhu, Xiaolin Hua, Jiangfeng Ye, Qian Chen i 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 (listopad 2021): e054540. http://dx.doi.org/10.1136/bmjopen-2021-054540.
Pełny tekst źródłaWan, 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 i in. "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 (październik 2018): e023070. http://dx.doi.org/10.1136/bmjopen-2018-023070.
Pełny tekst źródłaNdjaboue, Ruth, Gérard Ngueta, Charlotte Rochefort-Brihay, Daniel Guay, Sasha Delorme, Noah Ivers, Baiju Shah i in. "Risk Prediction Models of Diabetes Complications: A Scoping Review". Canadian Journal of Diabetes 45, nr 7 (listopad 2021): S32. http://dx.doi.org/10.1016/j.jcjd.2021.09.095.
Pełny tekst źródłaQian, Dongni, i 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.
Pełny tekst źródłaHD, Sowmya, Shreyaskar sanskar, Pawan tiwari i 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.
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