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Статті в журналах з теми "Diagnosis diabetes"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Inzucchi, Silvio E. "Diagnosis of Diabetes." New England Journal of Medicine 367, no. 6 (2012): 542–50. http://dx.doi.org/10.1056/nejmcp1103643.

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2

Bhatia, Jasvinder Kaur, and Jagdeep Singh Bhatia. "Evaluation of HbA1c in The Diagnosis of Diabetes." Annals of Applied Bio-Sciences 5, no. 2 (2018): A55–57. http://dx.doi.org/10.21276/aabs.2149.

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3

Nazarko, Linda. "Diabetes series, 2. Diagnosis of diabetes mellitus." British Journal of Healthcare Assistants 10, no. 9 (2016): 422–28. http://dx.doi.org/10.12968/bjha.2016.10.9.422.

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4

Skyrme, Tracey. "Devastated by diabetes diagnosis." Nursing Standard 19, no. 26 (2005): 24. http://dx.doi.org/10.7748/ns.19.26.24.s43.

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5

Cradock, Sue. "Diabetes mellitus at diagnosis." Nursing Standard 10, no. 30 (1996): 41–48. http://dx.doi.org/10.7748/ns.10.30.41.s48.

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6

M P, Ngugi, Njagi J M, Kibiti C M, Ngeranwa J J N, and Njagi E N M. "Diagnosis of Diabetes Mellitus." International Journal of Diabetes Research 1, no. 2 (2012): 24–27. http://dx.doi.org/10.5923/j.diabetes.20120102.01.

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7

Cha, Hyun-Hwa, Won Ki Lee, Sujeong Kim, Hyun Mi Kim, Mi Ju Kim, and Won Joon Seong. "Diagnosis of gestational diabetes." Medicine 101, no. 10 (2022): e29025. http://dx.doi.org/10.1097/md.0000000000029025.

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8

Coustan, Donald R. "Diagnosis of gestational diabetes." Scandinavian Journal of Clinical and Laboratory Investigation 74, sup244 (2014): 27–33. http://dx.doi.org/10.3109/00365513.2014.936677.

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9

Gray, D. Pereira, and P. Evans. "The diagnosis of diabetes." Diabetic Medicine 24, no. 9 (2007): 1051–52. http://dx.doi.org/10.1111/j.1464-5491.2007.02181.x.

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10

GOLDEN, WILLIAM E., and ROBERT H. HOPKINS. "HbA1c for Diabetes Diagnosis." Internal Medicine News 42, no. 21 (2009): 23. http://dx.doi.org/10.1016/s1097-8690(09)70868-6.

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11

Tormey, W. P., and T. M. Moore. "Diabetes diagnosis and autopsy." Diabetic Medicine 29, no. 11 (2012): 1470–71. http://dx.doi.org/10.1111/j.1464-5491.2012.03642.x.

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12

Forth, Rachel, and Edward Jude. "Diabetes: pathogenesis and diagnosis." British Journal of Healthcare Management 16, no. 12 (2010): 569–73. http://dx.doi.org/10.12968/bjhc.2010.16.12.569.

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13

Jidda, Ahmad. "Application of Machine Learning in Genomic-Based Diabetes Diagnosis." International Journal of Research Publication and Reviews 5, no. 12 (2024): 1880–85. https://doi.org/10.55248/gengpi.5.1224.3511.

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14

Wiener, K. "The Diagnosis of Diabetes Mellitus, Including Gestational Diabetes." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 29, no. 5 (1992): 481–93. http://dx.doi.org/10.1177/000456329202900502.

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15

Thornley, Simon, Craig Wright, Roger Marshall, et al. "Can the prevalence of diagnosed diabetes be estimated from linked national health records? The validity of a method applied in New Zealand." Journal of Primary Health Care 3, no. 4 (2011): 262. http://dx.doi.org/10.1071/hc11262.

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Анотація:

INTRODUCTION: With projected global increases in the prevalence of Type 2 diabetes, the health sector requires timely assessments of the prevalence of this disease to monitor trends, plan services, and measure the efficacy of prevention programmes. AIM: To assess the validity of a method to estimate the prevalence of diagnosed diabetes from linked national health records. METHODS: We measured the agreement between a diabetes diagnosis (using combined national lists of drug dispensing, outpatient attendance, laboratory tests (HbA1c) and hospital diagnoses) and a primary care diabetes diagnosis

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16

Refardt, Julie. "Diagnosis and differential diagnosis of diabetes insipidus: Update." Best Practice & Research Clinical Endocrinology & Metabolism 34, no. 5 (2020): 101398. http://dx.doi.org/10.1016/j.beem.2020.101398.

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17

Zheng, Mingyue, Carla De Oliveira Bernardo, Nigel Stocks, and David Gonzalez-Chica. "Diabetes Mellitus Diagnosis and Screening in Australian General Practice: A National Study." Journal of Diabetes Research 2022 (March 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/1566408.

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Анотація:

Aims. To investigate the epidemiology of diabetes diagnosis and screening in Australian general practice. Methods. Cross-sectional study using electronic health records of 1,522,622 patients aged 18+ years attending 544 Australian general practices (MedicineInsight database). The prevalence of diagnosed diabetes and diabetes screening was explored using all recorded diagnoses, laboratory results, and prescriptions between 2016 and 2018. Their relationship with patient sociodemographic and clinical characteristics was also investigated. Results. Overall, 7.5% (95% CI 7.3, 7.8) of adults had dia

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18

Dubois-Laforgue, Danièle, and José Timsit. "The Etiological Diagnosis of Diabetes: Still a Challenge for the Clinician." Endocrines 4, no. 2 (2023): 437–56. http://dx.doi.org/10.3390/endocrines4020033.

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The etiological diagnosis of diabetes conveys many practical consequences for the care of patients, and often of their families. However, a wide heterogeneity in the phenotypes of all diabetes subtypes, including Type 1 diabetes, Type 2 diabetes, and monogenic diabetes, has been reported and contributes to frequent misdiagnoses. The recently revised WHO classification of diabetes mellitus includes two new classes, namely “hybrid forms” and “unclassified diabetes”, which also reflect the difficulties of this etiological diagnosis. During the last years, many studies aiming at identifying homoge

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19

Farajollahi, Boshra, Maysam Mehmannavaz, Hafez Mehrjoo, Fateme Moghbeli, and Mohammad Javad Sayadi. "Diabetes Diagnosis Using Machine Learning." Frontiers in Health Informatics 10, no. 1 (2021): 65. http://dx.doi.org/10.30699/fhi.v10i1.267.

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Анотація:

Introduction: Diabetes is a disease associated with high levels of glucose in the blood. Diabetes make many kinds of complications, which also leads to a high rate of repeated admission of patients with diabetes. The aim of this study is to diagnose Diabetes with machine learning techniques.Material and Methods: The datasets of the article contain several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age. The main objective of the machine learning models is to classify of the d

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20

Sanke, Tokio. "Clinical Diagnosis for Diabetes Mellitus." Health evaluation and promotion 42, no. 2 (2015): 293–300. http://dx.doi.org/10.7143/jhep.42.293.

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21

Zimmet, Paul. "The diagnosis of diabetes mellitus." Medical Journal of Australia 145, no. 2 (1986): 88. http://dx.doi.org/10.5694/j.1326-5377.1986.tb101085.x.

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22

Ali, Ameer, Mohammed A. T. Alrubei, Laith Falah Mohammed Hassan, Mohannad A. M. Al-Ja'afari, and Saif H. Abdulwahed. "DIABETES DIAGNOSIS BASED ON KNN." IIUM Engineering Journal 21, no. 1 (2020): 175–81. http://dx.doi.org/10.31436/iiumej.v21i1.1206.

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Diabetes is a life-threatening syndrome occurring around the world; it can have huge complications and is documented by large amounts of medical data. Therefore, attempts at early detection of this disease took a large area of research and many methods were used to deal with diabetes. In this paper, different types of KNN algorithm have been used to classify diabetes disease using Matlab. The dataset was generated by the criteria of the American diabetes association. For the training stage, 4900 samples have been used by the classifier learner tool to observe the results. Then, 100 of the data

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23

Son, Hyun Shik. "Early Diagnosis of Diabetes Mellitus." Journal of the Korean Medical Association 51, no. 9 (2008): 813. http://dx.doi.org/10.5124/jkma.2008.51.9.813.

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24

Martin, F. I. R., Alan Clague, Richard Dargaville, Christopher Ericksen, Jeremy Oats, and Christine Tippett. "The diagnosis of gestational diabetes." Medical Journal of Australia 155, no. 2 (1991): 112. http://dx.doi.org/10.5694/j.1326-5377.1991.tb142140.x.

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25

RYAN, EDMOND A. "Clinical Diagnosis of Gestational Diabetes." Clinical Obstetrics and Gynecology 56, no. 4 (2013): 774–87. http://dx.doi.org/10.1097/grf.0b013e3182a8e029.

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26

Kruger, Davida F. "Managing Diabetes From First Diagnosis." Diabetes Educator 38, no. 4_suppl (2012): 4S—11S. http://dx.doi.org/10.1177/0145721712450619.

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27

Zendjabil, M. "Biological diagnosis of diabetes mellitus." Current Research in Translational Medicine 64, no. 1 (2016): 49–52. http://dx.doi.org/10.1016/j.patbio.2015.10.002.

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28

Alassaf, Abeer, and Rasha Odeh. "Delayed Diagnosis of Monogenic Diabetes." Canadian Journal of Diabetes 42, no. 5 (2018): S57. http://dx.doi.org/10.1016/j.jcjd.2018.08.175.

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29

Emmerson, A. J. B., and D. C. L. Savage. "Height at diagnosis in diabetes." European Journal of Pediatrics 147, no. 3 (1988): 319–20. http://dx.doi.org/10.1007/bf00442707.

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30

WILKE, W. S., and S. S. K. REDDY. "New guidelines streamline diabetes diagnosis." Cleveland Clinic Journal of Medicine 65, no. 1 (1998): 10–12. http://dx.doi.org/10.3949/ccjm.65.1.10.

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31

Cheng, Y. W., and A. B. Caughey. "Gestational diabetes: diagnosis and management." Journal of Perinatology 28, no. 10 (2008): 657–64. http://dx.doi.org/10.1038/jp.2008.62.

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32

Di Iorgi, Natascia, Flavia Napoli, Anna Elsa Maria Allegri, et al. "Diabetes Insipidus Diagnosis and Management." Hormone Research in Paediatrics 77, no. 2 (2012): 69–84. http://dx.doi.org/10.1159/000336333.

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33

Nash, Jen. "Dealing with diagnosis of diabetes." Practical Diabetes 32, no. 1 (2015): 19–23. http://dx.doi.org/10.1002/pdi.1915.

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34

Berlinger, Frederick G. "Diabetes Mellitus: Diagnosis and Treatment." JAMA: The Journal of the American Medical Association 266, no. 10 (1991): 1423. http://dx.doi.org/10.1001/jama.1991.03470100115047.

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35

Izenstein, Barry Z. "Diabetes Mellitus: Diagnosis and Treatment." JAMA: The Journal of the American Medical Association 257, no. 6 (1987): 850. http://dx.doi.org/10.1001/jama.1987.03390060140048.

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36

Laji, Ken. "Preventing missed diagnosis of diabetes." Practical Diabetes International 24, no. 3 (2007): 123. http://dx.doi.org/10.1002/pdi.1071.

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37

N, Sadhasivam, Harish J, and Bharanidharan M. "Diabetes Diagnosis using Machine Learning." December 2023 5, no. 4 (2023): 388–400. http://dx.doi.org/10.36548/jtcsst.2023.4.002.

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Анотація:

This abstract presents a study on utilizing the Gradient Boosting algorithm for diabetes diagnosis. The objective is to develop a reliable and effective model that uses patient data, to detect the presence of diabetes. For training and testing, a dataset made up of clinical parameters like age, body mass index, blood pressure, and glucose levels are used. The Gradient Boosting algorithm is implemented and optimized to achieve optimal predictive performance. The model's accuracy, precision, recall, and F1 score are evaluated to assess its effectiveness. The results of this study indicate that t

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38

Reham, Awdah Albalawi Hamdan Dhafer Alshehri Sultana Sedki H. Alnejaimi Sultan Mousa Bakri Anas Hassan Alzahrani Ashwaq Azhari Banjar Alaa Mohammed Alshahrani Munira Abdullah AlHashim Deema Ibrahim Altamimi Johar Mohammed Aljohar Dhiyaa Majed Shahadah. "DIABETES MELLITUS: SCREENING AND DIAGNOSIS." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 12 (2018): 14027–32. https://doi.org/10.5281/zenodo.1845091.

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<strong><em>Introduction:</em></strong><em> The term ‘diabetes mellitus’ refers to a wide range of metabolic conditions where there is an abnormal increase of glucose levels in the blood that results from dysfunctional insulin secretion and/or activity. Recent reports estimate that within the next twenty years, the US will have more than forty-four million patients diagnosed with diabetes mellitus type 2, which is almost double the current number or diabetic patients. Generally, any individual should be suspected to have diabetes mellitus type 2 if they have an indicative history.

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39

Roche, Madonna M., and Peizhong Peter Wang. "Factors associated with a diabetes diagnosis and late diabetes diagnosis for males and females." Journal of Clinical & Translational Endocrinology 1, no. 3 (2014): 77–84. http://dx.doi.org/10.1016/j.jcte.2014.07.002.

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40

Olsson, H., B. Attner, D. Noreen, and T. Lithman. "Comorbidity prior to diagnosis in patients with common cancer diagnoses." Journal of Clinical Oncology 27, no. 15_suppl (2009): e22180-e22180. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e22180.

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e22180 Background: Chronic disease as diabetes, hypertonia and anemia may be associated with cancer risk as well as affect the short term survival of the malignancy. Methods: Using population based registry data from specialist and primary care in our health care region comorbidity in the form of anemia, hypertonia, diabetes, rheumatoid arthritis, chronic obstructive pulmonary diasease (KOL), and alcohol related diseases for patients with colon-, rectal-, lung-, prostate and breast cancer and survival were studied. Altogether 2047 colon cancer cases, 985 rectal cancer cases, 2017 lung cancer c

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41

Makhmudov, Nurillo, and Zukhra Kosimova. "Diagnosis And Treatment Of Emphysematous Pyelonephritis In Patients With Diabetes." American Journal of Medical Sciences and Pharmaceutical Research 03, no. 01 (2021): 87–90. http://dx.doi.org/10.37547/tajmspr/volume03issue01-13.

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In this study we report on our experience in treating two patients diagnosed with a rare disease of emphysematous pyelonephritis, who were admitted to the Urology department of Fergana Medical Institute during 2018. We provide details on each test conducted on patients, pre- and post-surgery. Our analysis shows that to prevent complications in patients with diabetes mellitus, dispensary observation with control of glucose levels, urine and blood tests is necessary.

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42

El-Gamal, El-Khedr Mohamed Mostafa. "Preference of Glycatedhaemoglobinthan Glucose Profiles for the Diagnosis of Diabetes." Journal of Medical Science And clinical Research 05, no. 06 (2017): 24044–51. http://dx.doi.org/10.18535/jmscr/v5i6.204.

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43

Somani, B. L., S. S. Bangar, and R. Bhalwar. "American Diabetes Association criteria for diabetes diagnosis. Another perspective." Diabetes Care 22, no. 2 (1999): 366. http://dx.doi.org/10.2337/diacare.22.2.366b.

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44

Porcaro, Lorraine. "Diabetes, What's in a Name? Diabetes Diagnosis and Classification." Home Healthcare Now 42, no. 1 (2024): 52. http://dx.doi.org/10.1097/nhh.0000000000001234.

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45

Sultanova, S. S. "PREDIABETES: DIAGNOSIS AND TREATMENT." "The Medicine and Science" scientific-practical journal named after A.Aliyev 4, no. 30 (2023): 28–31. http://dx.doi.org/10.61775/2413-3302.v4i30.03.

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Анотація:

The article provides an overview of prediabetes, which precedes diabetes mellitus. It is at this stage that macro- and microvascular complications of diabetes develop. However, it is not always possible to identify this condition, since people with prediabetes have almost no clinical symptoms. At the same time, it is possible to suspect prediabetes. The article presents diagnostic criteria and methods of treatment of early disorders of carbohydrate metabolism. Timely detection of this pathology can prevent the development of diabetes mellitus and its complications.

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46

Lyu, Young Sang, Jin Hwa Kim, and Sang Yong Kim. "Differential Diagnosis of Hypoglycemia." Korean Journal of Medicine 96, no. 6 (2021): 484–92. http://dx.doi.org/10.3904/kjm.2021.96.6.484.

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Hypoglycemia is common but can lead to life-threatening consequences. Accurate diagnosis is important to establish the appropriate treatment strategy. Most cases of hypoglycemia are caused by hypoglycemic agents, although it can occur in individuals without diabetes. A systemic and comprehensive diagnostic approach is required to diagnose hypoglycemia in patients without diabetes. It is important to perform appropriate blood testing during an episode of hypoglycemia. This review will focus on the definition, differential diagnosis, causes, and treatment of hypoglycemia, particularly in people

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47

Venkatesan, Radha, V. Mohan, and Rakesh Kumar. "Genetics for the pediatric endocrinologists – 1." Journal of Pediatric Endocrinology and Diabetes 2 (May 3, 2022): 23–30. http://dx.doi.org/10.25259/jped_6_2022.

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The advancement in genetic laboratory technology has helped immensely in the diagnosis of many genetic disorders which could not hitherto be diagnosed. Monogenic diabetes among children and adolescents is not uncommon and needs a high index of clinical suspicion to diagnose. With the availability of genetic diagnostic laboratories with the latest technology, more and more patients should benefit from early diagnosis, specific targeted treatment, and better outcomes. The pediatricians and pediatric endocrinologists managing children with diabetes need to clinically suspect and advise appropriat

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48

Currie, Janet M., Michele Mercer, Russ Michael, and Daniel Pichardo. "New Caregiver Diagnoses of Severe Depression and Child Asthma Controller Medication Adherence." International Journal of Environmental Research and Public Health 20, no. 11 (2023): 5986. http://dx.doi.org/10.3390/ijerph20115986.

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Background and Objectives: Children with asthma who have depressed caregivers are known to be less adherent to medication regimes. However, it is less clear how adherence responds to a caregiver’s new diagnosis of severe depression or whether there is a similar relationship with other serious caregiver diagnoses. The hypothesis is that adherence worsens both with new diagnoses of depression and possibly with new diagnoses of other serious conditions. Methods: This study follows a cohort of 341,444 continuously insured children with asthma before and after a caregiver’s new diagnosis of severe

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49

Rao, Gundu HR. "Early Diagnosis and Management of Type-2 Diabetes: A Case Study." Open Access Journal of Cardiology 8, no. 1 (2024): 1–11. http://dx.doi.org/10.23880/oajc-16000209.

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Анотація:

Metabolic diseases, such as hypertension, obesity, type-2 diabetes, and vascular diseases, have reached epidemic proportions globally, contributing significantly to non-communicable disease (NCD)-related mortality. Non-communicable diseases account for 71% of global deaths annually, with cardiovascular diseases (CVDs) leading the statistics. The South Asian phenotype presents a unique set of clinical characteristics, including, typical visceral obesity, high triglycerides, increased low-density lipoprotein cholesterol (LDL-Cholesterol), insulin resistance, and high propensity for type-2 diabet

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50

Irianto. "Diagnosis dan Tata Laksana Diabetes Insipidus." Cermin Dunia Kedokteran 51, no. 10 (2024): 567–73. http://dx.doi.org/10.55175/cdk.v51i10.1234.

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Анотація:

Diabetes insipidus merupakan salah satu sindrom poliuria-polidipsia, ditandai dengan urin melebihi 50 mL/kgBB. disertai konsumsi cairan yang lebih tinggi. Diabetes insipidus dibagi menjadi 4 jenis: sentral, nefrogenik, dipsogenik, dan gestasional. Penyakit ini tergolong langka, sehingga sering tidak terdeteksi. Terapi berbeda sesuai tipe diabetes insipidus.

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