Статті в журналах з теми "Diabetes Complication predictions"
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Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler, and 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 (February 27, 2023): e42181. http://dx.doi.org/10.2196/42181.
Haber, 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, no. 4 (February 10, 2021): 685. http://dx.doi.org/10.3390/jcm10040685.
Tang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang, and Jinsheng Shi. "Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records." Sensors 22, no. 21 (November 3, 2022): 8454. http://dx.doi.org/10.3390/s22218454.
Alruwaytie, Wedad, Amal Mackawy, and 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, no. 1 (January 30, 2022): 716–20. http://dx.doi.org/10.53350/pjmhs22161716.
Zuo, Ming, Wei Zhang, Qi Xu, and Dehua Chen. "Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning." Journal of Healthcare Engineering 2022 (April 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/5129125.
Liu, Xiao-Chen, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang, and 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, no. 20 (July 16, 2024): 4048–56. http://dx.doi.org/10.12998/wjcc.v12.i20.4048.
Chen, Xiao, Min Hou, and Dongxue Wang. "Machine learning-based model for prediction of deep vein thrombosis after gynecological laparoscopy: A retrospective cohort study." Medicine 103, no. 1 (January 5, 2024): e36717. http://dx.doi.org/10.1097/md.0000000000036717.
Healthcare 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 (September 20, 2023): 1. http://dx.doi.org/10.1155/2023/9891682.
Dahab, Mahmoud, Ping Zhang, Samiah Hamad Al-Mijalli, and Emad M. Abdallah. "Unveiling the Anti-Cholera and Active Diabetic Renoprotective Compounds of Maqian Essential Oil: A Computational and Molecular Dynamics Study." Molecules 28, no. 24 (December 5, 2023): 7954. http://dx.doi.org/10.3390/molecules28247954.
Alghamdi, Turki. "Prediction of Diabetes Complications Using Computational Intelligence Techniques." Applied Sciences 13, no. 5 (February 27, 2023): 3030. http://dx.doi.org/10.3390/app13053030.
Yagin, Fatma Hilal, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan, and Luca Paolo Ardigò. "Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy." Diagnostics 14, no. 13 (June 27, 2024): 1364. http://dx.doi.org/10.3390/diagnostics14131364.
Veerabathiran, Ramakrishnan. "Macrosomia: A Serious Complication of Diabetes in Pregnancy." Diabetes & Obesity International Journal 8, no. 4 (2023): 1–6. http://dx.doi.org/10.23880/doij-16000280.
Assegie, Tsehay Admassu, Tamilarasi Suresh, Raguraman Purushothaman, Sangeetha Ganesan, and Napa Komal Kumar. "Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier." Journal of Robotics and Control (JRC) 4, no. 4 (July 4, 2023): 452–57. http://dx.doi.org/10.18196/jrc.v4i4.18412.
Bommala, Harikrishna, Kannedari Vamshi Krishna, Avusula Supriya, Rama Krishna Biradar, Bharath Mayabrahma, D. Ushasree, and 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.
Jian, Yazan, Michel Pasquier, Assim Sagahyroon, and Fadi Aloul. "A Machine Learning Approach to Predicting Diabetes Complications." Healthcare 9, no. 12 (December 9, 2021): 1712. http://dx.doi.org/10.3390/healthcare9121712.
Yousefi, Leila, and Allan Tucker. "Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction." Intelligent Data Analysis 26, no. 2 (March 14, 2022): 501–24. http://dx.doi.org/10.3233/ida-205570.
Brink, Huguette S., Aart Jan van der Lely, and Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes." Endocrine Connections 5, no. 5 (September 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
V, Sathya. "Maternal Serum Biomarkers for the Early Prediction of Gestational Diabetes Mellitus." Diabetes & Obesity International Journal 4, no. 1 (2019): 1–7. http://dx.doi.org/10.23880/doij-16000190.
Rachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak, and 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, no. 1 (June 23, 2019): 49–58. http://dx.doi.org/10.37936/ecti-cit.2019131.132114.
Hayuningtyas, Ratih Yulia, and Retno Sari. "Implementasi Data Mining Dengan Algoritma Multiple Linear Regression Untuk Memprediksi Penyakit Diabetes." Jurnal Teknik Komputer 8, no. 1 (January 24, 2022): 40–44. http://dx.doi.org/10.31294/jtk.v8i1.11552.
Rozhkova, O. V., O. V. Remneva, and N. V. Trukhacheva. "Prediction of perinatal complications of gestational diabetes." Fundamental and Clinical Medicine 4, no. 4 (December 28, 2019): 19–25. http://dx.doi.org/10.23946/2500-0764-2019-4-4-19-25.
Tetreault, Lindsay, Gamaliel Tan, Branko Kopjar, Pierre Côté, Paul Arnold, Natalia Nugaeva, Giuseppe Barbagallo, and Michael G. Fehlings. "Clinical and Surgical Predictors of Complications Following Surgery for the Treatment of Cervical Spondylotic Myelopathy." Neurosurgery 79, no. 1 (November 25, 2015): 33–44. http://dx.doi.org/10.1227/neu.0000000000001151.
Kathiravan A., Dr T. Ananth kumar, and 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, no. 04 (2023): 18–24. http://dx.doi.org/10.46759/iijsr.2023.7403.
Song, Xing, Lemuel Russ Waitman, Alan SL Yu, David C. Robbins, Yong Hu, and 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, no. 1 (January 24, 2020): e15510. http://dx.doi.org/10.2196/15510.
Kumari, Gorli L. Aruna, Poosapati Padmaja, and 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, no. 1 (April 1, 2022): 404. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp404-413.
Kartina Diah Kusuma Wardani and Memen Akbar. "Diabetes Risk Prediction using Feature Importance Extreme Gradient Boosting (XGBoost)." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (August 12, 2023): 824–31. http://dx.doi.org/10.29207/resti.v7i4.4651.
Gardner, 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 (January 2023): 205520762211286. http://dx.doi.org/10.1177/20552076221128677.
Saxena, Roshi, Sanjay Kumar Sharma, Manali Gupta, and G. C. Sampada. "A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey." Journal of Healthcare Engineering 2022 (April 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/8100697.
El-Sofany, Hosam, Samir A. El-Seoud, Omar H. Karam, Yasser M. Abd El-Latif, and 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 (January 9, 2024): 1–13. http://dx.doi.org/10.1155/2024/6688934.
Almheiri, Ali, Amna Alhammadi, Fatima AlShehhi, Asma Mohammad, Rodha Alshamsi, Khaled Alzaman, Saima Jabeen, and Burhan Haq. "Biomarkers for Prediabetes, Type 2 Diabetes, and Associated Complications." American Journal of Health, Medicine and Nursing Practice 9, no. 2 (September 27, 2023): 1–21. http://dx.doi.org/10.47672/ajhmn.1592.
Rasha Rokan Ismail. "Early diagnosing diabetes using data mining algorithms." Global Journal of Engineering and Technology Advances 16, no. 2 (August 30, 2023): 106–13. http://dx.doi.org/10.30574/gjeta.2023.16.2.0141.
Ginting, Rapael Ginting, Ermi Girsang, Johannes Bastira Ginting, and Hartono Hartono. "ANALISIS DETERMINAN DAN PREDIKSI PENYAKIT DIABETES MELITUS TIPE 2 MENGGUNAKAN METODE MACHINE LEARNING: SCOPING REVIEW." Jurnal Maternitas Kebidanan 7, no. 1 (April 16, 2022): 58–72. http://dx.doi.org/10.34012/jumkep.v7i1.2538.
Ziajor, 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 (April 11, 2024): 11–27. http://dx.doi.org/10.12775/jehs.2024.65.001.
Agliata, Antonio, Deborah Giordano, Francesco Bardozzo, Salvatore Bottiglieri, Angelo Facchiano, and Roberto Tagliaferri. "Machine Learning as a Support for the Diagnosis of Type 2 Diabetes." International Journal of Molecular Sciences 24, no. 7 (April 5, 2023): 6775. http://dx.doi.org/10.3390/ijms24076775.
Nijpels, Giel, Joline WJ Beulens, Amber AWA van der Heijden, and Petra J. Elders. "Innovations in personalised diabetes care and risk management." European Journal of Preventive Cardiology 26, no. 2_suppl (November 26, 2019): 125–32. http://dx.doi.org/10.1177/2047487319880043.
Tanabe, 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, no. 7 (July 2, 2020): 2083. http://dx.doi.org/10.3390/jcm9072083.
Lu, Huiqi Y., Ping Lu, Jane E. Hirst, Lucy Mackillop, and David A. Clifton. "A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus." Sensors 23, no. 18 (September 20, 2023): 7990. http://dx.doi.org/10.3390/s23187990.
Luo, Xin, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao, and 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, no. 8 (August 8, 2023): e0289749. http://dx.doi.org/10.1371/journal.pone.0289749.
Vijayan, Midhula, and Venkatakrishnan S. "A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet." Diagnostics 13, no. 4 (February 17, 2023): 774. http://dx.doi.org/10.3390/diagnostics13040774.
Lin, Ming-Yen, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang, and 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, no. 8 (August 12, 2021): 326. http://dx.doi.org/10.3390/info12080326.
CEVHER AKDULUM, Munire Funda, Erhan DEMİRDAĞ, Safarova SAHİLA, Mehmet ERDEM, and Ahmet ERDEM. "İlk Trimesterde Sistemik İmmün-İnflamasyon İndeksini Kullanarak Gestasyonel Diabetes Mellitus'u Tahmin Etme." Journal of Contemporary Medicine 12, no. 5 (September 30, 2022): 617–20. http://dx.doi.org/10.16899/jcm.1148179.
Abdalrada, Ahmad Shaker, Jemal Abawajy, Tahsien Al-Quraishi, and 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 (January 2022): 204201882210866. http://dx.doi.org/10.1177/20420188221086693.
Febriani, Irene. "Undiagnosed Diabetes Prediction With Development of Scoring System Based on Risk Factors." Preventif : Jurnal Kesehatan Masyarakat 11, no. 1 (August 3, 2020): 9–21. http://dx.doi.org/10.22487/preventif.v11i1.54.
Jose, Rejath, Faiz Syed, Anvin Thomas, and Milan Toma. "Cardiovascular Health Management in Diabetic Patients with Machine-Learning-Driven Predictions and Interventions." Applied Sciences 14, no. 5 (March 4, 2024): 2132. http://dx.doi.org/10.3390/app14052132.
Han, Yiteng, Qixuan Li, Jinghui Lou, and Jingrui Zhang. "Prediction of diabetes progress based on machine learning approach." Applied and Computational Engineering 37, no. 1 (January 22, 2024): 45–51. http://dx.doi.org/10.54254/2755-2721/37/20230468.
Zhan, Wenqiang, Jing Zhu, Xiaolin Hua, Jiangfeng Ye, Qian Chen, and 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, no. 11 (November 2021): e054540. http://dx.doi.org/10.1136/bmjopen-2021-054540.
Wan, 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, no. 10 (October 2018): e023070. http://dx.doi.org/10.1136/bmjopen-2018-023070.
Ndjaboue, 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, no. 7 (November 2021): S32. http://dx.doi.org/10.1016/j.jcjd.2021.09.095.
Qian, Dongni, and 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 (July 29, 2022): 1–7. http://dx.doi.org/10.1155/2022/3882425.
HD, Sowmya, Shreyaskar sanskar, Pawan tiwari, and Kishan kumar. "Diabetes Prediction Using Machine Learning Algorithm." International Journal of Innovative Research in Information Security 09, no. 03 (June 23, 2023): 115–20. http://dx.doi.org/10.26562/ijiris.2023.v0903.14.