Relevant bibliographies by topics / Diabetes Complication predictions

Academic literature on the topic 'Diabetes Complication predictions'

Author: Grafiati
Published: 7 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Diabetes Complication predictions.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Diabetes Complication predictions":

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Background Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. Objective This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. Methods In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. Results Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. Conclusions Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Minimal-invasive techniques are increasingly applied in clinical practice and have contributed towards improving postoperative outcomes. While comparing favorably with open surgery in terms of safety, the occurrence of severe complications remains a grave concern. To date, no objective predictive system has been established to guide clinicians in estimating complication risks as the relative contribution of general patient health, liver function and surgical parameters remain unclear. Here, we perform a single-center analysis of all consecutive patients undergoing laparoscopic liver resection for primary hepatic malignancies since 2010. Among the 210 patients identified, 32 developed major complications. Several independent predictors were identified through a multivariate analysis, defining a preoperative model: diabetes, history of previous hepatectomy, surgical approach, alanine aminotransferase levels and lesion entity. The addition of operative time and whether conversion was required significantly improved predictions and were thus incorporated into the postoperative model. Both models were able to identify patients with major complications with acceptable performance (area under the receiver-operating characteristic curve (AUC) for a preoperative model = 0.77 vs. postoperative model = 0.80). Internal validation was performed and confirmed the discriminatory ability of the models. An easily accessible online tool was deployed in order to estimate probabilities of severe complication without the need for manual calculation.
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and real-time prediction of blood glucose concentrations after each short-acting insulin injection has great clinical significance and is the basis of all intelligent blood glucose control systems. Most previous prediction methods require long-term continuous blood glucose records from specific patients to train the prediction models, resulting in these methods not being used in clinical practice. In this study, we construct 13 deep neural networks with different architectures to atomically predict blood glucose concentrations after arbitrary independent insulin injections without requiring continuous historical records of any patient. Using our proposed models, the best root mean square error of the prediction results reaches 15.82 mg/dL, and 99.5% of the predictions are clinically acceptable, which is more accurate than previously proposed blood glucose prediction methods. Through the re-validation of the models, we demonstrate the clinical practicability and universal accuracy of our proposed prediction method.
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Background: Diabetic nephropathy (DN) is a dangerous illness associated with a significant risk for cardiovascular and kidney problems in diabetic patients. Serum cystatin C levels may rise in diabetic individuals with microalbuminuria, and it has been recommended as an endogenous glomerular filtration rate (GFR) marker because of its link to the albumin to creatinine ratio (ACR) in diabetic nephropathy. Uncontrolled diabetes was found to have a greater level of fibrinogen; in diabetic nephropathy, fibrinogen levels are important. In addition, fibrinogen has been associated to inflammation and has been demonstrated to play a crucial pathophysiologic role in the advancement of renal impairment in individuals. Methods: The author has no intention of commenting on the molecular role of cystatin C, a cysteine protease inhibitor, or the disrupted haemostatics mechanism in fibrinogen-induced diabetes. in this study, which is expected to investigate views on using the cystatin C as well as plasma fibrinogen plasma levels like an early markers of nephrotic syndrome. Therefore, 4 important clinical datasets were reviewed, including the EMBASE, PubMed and The Cochrane Library, Medline, Google Scholar and a few additional related journals datasets, as well as relevant records were collected with high precision.‎ Conclusion: When compared to the frequently used creatinine-based predictions, cystatin C is a good marker for diagnosing nephropathy in patients with normal albuminuria, and it may enhance the risk prediction in diabetics. Even before the complication of chronic kidney disease symptoms, Cystatin C levels in urine might be raised in diabetic individuals. Additional investigation into cystatin C and fibrinogen functions as early biomarkers, clinical value in screening, involvement in prognosis, decrease of inflammation and prediction of medication clearance, and drug monitoring in type II diabetic, nephropathy is needed.‎ Keywords: cystatin C, fibrinogen, nephropathy, diabetes mellitus, biomarkers
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Objective. Diabetic complications have brought a tremendous burden for diabetic patients, but the problem of predicting diabetic complications is still unresolved. Our aim is to explore the relationship between hemoglobin A1C (HbA1c), insulin (INS), and glucose (GLU) and diabetic complications in combination with individual factors and to effectively predict multiple complications of diabetes. Methods. This was a real-world study. Data were collected from 40,913 participants with an average age of 48 years from the Department of Endocrinology of Ruijin Hospital in Shanghai. We proposed deep personal multitask prediction of diabetes complication with attentive interactions (DPMP-DC) to predict the five complication models of diabetes, including diabetic retinopathy, diabetic nephropathy, diabetic peripheral neuropathy, diabetic foot disease, and diabetic cardiovascular disease. Results. Our model has an accuracy rate of 88.01% for diabetic retinopathy, 89.58% for diabetic nephropathy, 85.77% for diabetic neuropathy, 80.56% for diabetic foot disease, and 82.48% for diabetic cardiovascular disease. The multitasking accuracy of multiple complications is 84.67%, and the missed diagnosis rate is 9.07%. Conclusion. We put forward the method of interactive integration with individual factors of patients for the first time in diabetic complications, which reflect the differences between individuals. Our multitask model using the hard sharing mechanism provides better prediction than prior single prediction models.
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes. AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model. METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed. RESULTS Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839–0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively. CONCLUSION Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Successful monitoring of deep vein thrombosis (DVT) remains a challenging problem after gynecological laparoscopy. Thus, this study aimed to create and validate predictive models for DVT with the help of machine learning (ML) algorithms. A total of 489 patients from the Cancer Biology Research Center, Tongji Hospital were included in the study between January 2017 and February 2023, and 35 clinical indicators from electronic health records (EHRs) were collected within 24h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. Then, the three commonly used DVT prediction models are random forest model (RFM), generalized linear regression model (GLRM), and artificial neural network model (ANNM). In addition, the predictive performance of various prediction models (i.e. the robustness and accuracy of predictions) is evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively. We found postoperative DVT in 41 (8.38%) patients. Based on the ML algorithm, a total of 13 types of clinical data were preliminarily screened as candidate variables for DVT prediction models. Among these, age, body mass index (BMI), operation time, intraoperative pneumoperitoneum pressure (IPP), diabetes, complication and D-Dimer independent risk factors for postoperative DVT and can be used as variables in ML prediction models. The RFM algorithm can achieve the optimal DVT prediction performance, with AUC values of 0.851 (95% CI: 0.793–0.909) and 0.862 (95% CI: 0.804–0.920) in the training and validation sets, respectively. The AUC values of the other two prediction models (ANNM and GLRM) range from 0.697 (95% CI: 0.639–0.755) and 0.813 (95% CI: 0.651–0.767). In summary, we explored the potential risk of DVT after gynecological laparoscopy, which helps clinicians identify high-risk patients before gynecological laparoscopy and make nursing interventions. However, external validation will be needed in the future.
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Cholera is an exceptionally aggressive infectious disease characterized by the potential to induce acute, copious, watery diarrhea of considerable severity and renal inflammation. Diabetic nephropathy is a serious complication of diabetes mellitus that can lead to kidney failure through inflammation; thus, anti-inflammatory agents are promising therapies for diabetic nephropathy. Previous studies have shown that the essential oil of Zanthoxylum myriacanthum var. pubescens Huang, Maqian essential oil (MQEO), exhibits potent antibacterial, anti-inflammatory, and renoprotective activities in diabetic mice and has emerged as a potential therapeutic drug for the treatment of diabetic nephropathy complications. Therefore, the present study was carried out to screen the potential inhibition of cholera toxin and the diabetic renoprotective activity of MQEO through computational approaches. Twelve chemical constituents derived from MQEO were docked with cholera toxin and the target proteins involved in diabetic nephropathy, namely, TXNIP, Nrf2, and DPP IV, and, subsequently, the predictions of molecular dynamic simulations, the drug-likeness properties, and the ADMET properties were performed. α-terpineol showed high binding affinities toward the cholera toxin protein. For TXNIP, among all the chemical constituents, α-phellandrene and p-cymene showed strong binding affinities with the TXNIP protein and displayed relatively stable flexibility at the hinge regions of the protein, favorable physicochemical properties in the absence of hepatotoxicity, and low cytotoxicity. For Nrf2, α-terpineol exhibited the highest binding affinity and formed a very stable complex with Nrf2, which displayed high pharmacokinetic properties. All compounds had low free-binding energies when docked with the DPP IV protein, which suggests potent biological activity. In conclusion, based on a computational approach, our findings reveal that MQEO constituents have inhibitory activity against cholera toxin and are promising therapeutic agents for suppressing diabetic inflammation and for the treatment of diabetic nephropathy complications.
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%.
More sources
You might also be interested in the extended bibliographies on the topic 'Diabetes Complication predictions' for particular source types:
Journal articles

Dissertations / Theses on the topic "Diabetes Complication predictions":

1

Toofanee, Mohammud Shaad Ally. "An innovative ecosystem based on deep learning : Contributions for the prevention and prediction of diabetes complications." Electronic Thesis or Diss., Limoges, 2023. https://aurore.unilim.fr/theses/nxfile/default/656b0a1f-2ff2-49c5-bb3e-f34704d6f6b0/blobholder:0/2023LIMO0107.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
En 2021, le diabète touchait environ 537 millions de personnes dans le monde. Ce chiffre devrait grimper à 643 millions d'ici 2030 et 783 millions d'ici 2045. Le diabète est une maladie métabolique persistante qui nécessite des soins et une gestion quotidiens continus. Le fardeau des maladies chroniques pèse lourdement sur les systèmes de santé lorsqu'il touche une partie substantielle de la population. De telles circonstances ont un impact négatif non seulement sur le bien-être général d'une grande partie de la population, mais contribuent également de manière significative aux dépenses de santé. Dans le contexte de Maurice, selon le rapport le plus récent de la Fédération Internationale du Diabète, la prévalence du diabète, en particulier du diabète de type 2 (T2D), était de 22,6 % de la population en 2021, avec des projections indiquant une hausse à 26,6 % d'ici 2045. Face à cette tendance alarmante, une évolution concomitante a été observée dans le domaine de la technologie, l'intelligence artificielle démontrant des capacités prometteuses dans les domaines de la médecine et de la santé. Cette thèse de doctorat entreprend l'exploration de l'intersection entre l'intelligence artificielle, plus précisément l’apprentissage profond, l'éducation, la prévention, et la gestion du diabète. Nous nous sommes d'abord concentrés sur l'exploration du potentiel de l'Intelligence Artificielle (IA) pour répondre à une complication fréquente du diabète : l'Ulcère du Pied Diabétique (DFU). Les DFU présentent un risque grave d'amputations des membres inférieurs, entraînant des conséquences graves. En réponse, nous avons proposé une solution innovante nommée DFU-HELPER. Cet outil permet de valider les protocoles de traitement administrés par les professionnels de la santé aux patients individuels atteints de DFU. L'évaluation initiale de l'outil a montré des résultats prometteurs, bien qu'un affinement further et des tests rigoureux soient impératifs. Les efforts collaboratifs avec les experts en santé publique seront essentiels pour évaluer l'efficacité pratique de l'outil dans des scénarios réels. Cette approche vise à combler le fossé entre les technologies IA et les interventions cliniques, avec pour objectif ultime d'améliorer la prise en charge des patients atteints de DFU. Notre recherche a également abordé les aspects critiques de la vie privée et de la confidentialité inhérents à la manipulation des données liées à la santé. Reconnaissant l'importance capitale de la protection des informations sensibles, nous avons appliqué une approche avancée d'apprentissage fédéré Peer-to-Peer à notre proposition pour l'outil DFU-Helper. Cette approche permet de traiter des données sensibles sans les transférer vers un serveur central, contribuant ainsi à créer un environnement de confiance et sécurisé pour la gestion des données de santé. Enfin, notre recherche s'est étendue au développement d'un agent conversationnel intelligent conçu pour fournir des informations et un soutien 24 heures sur 24 aux personnes atteintes de diabète. Dans la poursuite de cet objectif, la création d'un jeu de données approprié était essentielle. Dans ce contexte, nous avons utilisé des techniques de traitement du langage naturel pour sélectionner des données de qualité provenant de sources médias en ligne traitant du diabète
In the year 2021, estimations indicated that approximately 537 million individuals were affected by diabetes, a number anticipated to escalate to 643 million by the year 2030 and further to 783 million by 2045. Diabetes, characterized as a persistent metabolic ailment, necessitates unceasing daily care and management. In the context of Mauritius, as per the most recent report by the International Diabetes Federation, the prevalence of diabetes, specifically Type 2 Diabetes (T2D), stood at 22.6% of the population in 2021, with projections indicating a surge to 26.6% by the year 2045. Amidst this alarming trend, a concurrent advancement has been observed in the realm of technology, with artificial intelligence techniques showcasing promising capabilities in the spheres of medicine and healthcare. This doctoral dissertation embarks on the exploration of the intersection between artificial intelligence and diabetes education, prevention, and management.We initially focused on exploring the potential of artificial intelligence (AI), more specifically, deep learning, to address a critical complication linked to diabetes – Diabetic Foot Ulcer (DFU). The emergence of DFU poses the grave risk of lower limb amputations, consequently leading to severe socio-economic repercussions. In response, we put forth an innovative solution named DFU-HELPER. This tool serves as a preliminary measure for validating the treatment protocols administered by healthcare professionals to individual patients afflicted by DFU. The initial assessment of the proposed tool has exhibited promising performance characteristics, although further refinement and rigorous testing are imperative. Collaborative efforts with public health experts will be pivotal in evaluating the practical efficacy of the tool in real-world scenarios. This approach seeks to bridge the gap between AI technologies and clinical interventions, with the ultimate goal of improving the management of patients with DFU.Our research also addressed the critical aspects of privacy and confidentiality inherent in handling health-related data. Acknowledging the extreme importance of safeguarding sensitive information, we delved into the realm of Peer-to-Peer Federated Learning. This investigation specifically found application in our proposal for the DFU-Helper tool discussed earlier. By exploring this advanced approach, we aimed to ensure that the implementation of our technology aligns with privacy standards, thereby fostering a trustworthy and secure environment for healthcare data management.Finally, our research extended to the development of an intelligent conversational agent designed to offer round-the-clock support for individuals seeking information about diabetes. In pursuit of this goal, the creation of an appropriate dataset was paramount. In this context, we leveraged Natural Language Processing techniques to curate data from online media sources focusing on diabetes-related content
2

Moutairou, Abdul Kayoum. "Les hypoglycémies et leurs conséquences dans l’essai DCCT Short-term effect of severe hypoglycaemia on glycaemic control in the Diabetes Control and Complications Trial Predicting severe hypoglycaemia with self-monitoring of blood glucose in type 1 diabetes Non-severe hypoglycaemia is associated with weight gain in patients with type 1 diabetes: Results from the Diabetes Control and Complication Trial." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS251.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Avec une incidence de plus en plus élevée, le diabète de type 1 (DT1) constitue un problème majeur de santé. Le contrôle glycémique est la mesure nécessaire pour la prévention des complications chroniques du DT1. Cependant, ce contrôle a pour conséquences la survenue des effets secondaires comme l’hypoglycémie et la prise de poids, avec des impacts négatifs significatifs sur la santé et la qualité de vie des patients. A partir des données de l’essai DCCT (Diabetes Control Complications Trial) réalisé entre les Etats Unis et le Canada de 1983 à 1993, qui a inclus 1441 patients atteints de DT1, nous avons étudié les déterminants et les conséquences de l’hypoglycémie (sévère ou non). Dans un premier temps, nous avons étudié la variation de l’hémoglobine glyquée (HbA1c) après un épisode d’hypoglycémie sévère (HS). Cette étude nous a permis de montrer que la survenue d’une HS a été suivie d'une augmentation modeste mais significative de l'HbA1c 3 mois plus tard. Ce résultat suggère que l’hypoglycémie sévère pourrait constituer un obstacle majeur à la réalisation d'un contrôle glycémique optimal chez les patients atteints de DT1 et doit être prise en compte dans les programmes éducatifs et thérapeutiques chez ces patients. Dans un deuxième temps, nous avons étudié l’association entre la fréquence de l’hypoglycémie non sévère détectée par auto-surveillance de la glycémie (SMBG) et l’incidence des hypoglycémies sévères. Il a été établi que l'hypoglycémie non sévère est un facteur prédictif majeur des épisodes d’hypoglycémie sévère chez les patients atteints de DT1. Les données sur l’auto-surveillance glycémique du DCCT sont très similaires aux pratiques actuelles en matière de soins de routine et nos résultats renforcent les stratégies préventives actuelles en matière d’hypoglycémie sévère chez les patients atteints de DT1. Enfin, dans une troisième partie, nous mis en évidence une association entre les hypoglycémies non sévères et le gain de poids dans le DT1. Nous avons observé qu'un taux plus élevé d'hypoglycémies non sévères, basé sur l’auto-surveillance glycémique, était significativement associé à une prise de poids plus importante, indépendamment des autres facteurs comme sur le sexe, l'âge, la durée du diabète, la variation de l’HbA1c et le type de traitement. Cette conséquence négative nécessite la mise place d’une stratégie de prise en charge des hypoglycémies et des programmes d'éducation spécifiques visant à réduire le gain de poids dans le DT1
With an increasing incidence, type 1 diabetes is a major health problem. Glycemic control is the necessary measure for the prevention of chronic complications of type 1 diabetes. However, this control is associated with side effects such as hypoglycemia and weight gain, so with significant negative impacts on health and the quality of life of patients. Based on data from DCCT (Diabetes Control Complications Trial), the United States-Canada trials from 1983 to 1993, which included 1441 patients with type 1 diabetes, we examined the determinants and the impact of hypoglycemia (severe or not), in patients with type 1 diabetes. First, we studied the variation of glycated hemoglobin (HbA1c) after an episode of severe hypoglycaemia (SH). We showed that the occurrence of SH was followed by a modest but significant increase in HbA1c 3 months later in all participants. This result suggests that severe hypoglycaemia may be a major barrier to achieving optimal glycemic control in patients with T1D, and should be considered in educational and therapeutic programs for these patients. Second, we studied the association between the frequency of non-severe hypoglycemia detected by self-monitoring of blood glucose (SMBG) and the incidence of severe hypoglycaemia. Non-severe hypoglycaemia has been shown to be a major predictor of episodes of severe hypoglycaemia in patients with T1D. Data on glycemic self-monitoring of DCCT is very similar to current routine care practices and our results reinforce current preventive strategies for severe hypoglycemia in T1D patients. Third, we investigated the association between non-severe hypoglycemia and weight gain in type 1 diabetes. Higher rate of non-severe hypoglycemia, based on self-monitoring of blood glucose, was significantly associated with greater weight gain. This association was independent of factors such as sex, age, duration of diabetes, change in HbA1c and type of treatment. This negative consequence requires the implementation of a hypoglycemic management strategy and specific educational programs aimed at reducing weight gain in type 1 diabetes
3

Tseng, Chun Jen, and 曾俊仁. "Risk prediction of complication incidence on chronic kidney disease for type II diabetes." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/29ydg6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
碩士
長庚大學
醫務管理學系
104
Background The prevalence rate of Type 2 Diabetes (T2DM) is dramatically increasing in Taiwan which demonstrated 7.1% prevalence according the statistic report in 2012. Among those T2DM patients, one-third subjects will develop as chronic kidney disease (CKD) in the life time, which is highly associated with diabetes control. The burden of diabetes leads the complications to higher medical expenditure, especially for the chronic kidney disease and end-stage of renal disease (ESRD). Based on the primary clinic care with appropriate education and surveillance for disease controlling, the incidence rates and severity of complications would be reduced. However, regarding the risk of CKD prediction, there was no substantial study to investigate the risk factors on CKD development based on Taiwanese diabetes patients. Furthermore, the optimal period of surveillance for CKD is still controversial, but it is essential issue for healthcare policy making. Aim There were two parts in our study, first, we aimed to estimate the mean sojourn time (MST), also call dwelling time, of CKD using the prevalence and incidence rate of CKD (P/I ratio) approach based on the database of shared care for diabetes. Second, we conducted the prospective cohort design based on shared care of diabetes in Keelung Chang Gung Memorial Hospital to investigate the predictive risk factors for CKD incidence. Materials and Methods Based on the database of Shared Care for Diabetes in Keelung Chang Gung Memorial Hospital, that Type 1 diabetes cases were excluded from our study. Those who CKD history by self-report or baseline value of eGFR less than 60 were defined as prevalent cases of CKD that contributed for prevalence calculation. Excluding those prevalent CKD cases, others with 2 or more clinic visits were followed up to last clinic visit or the end of 2014. For the incidence cases, the follow-up time were calculated till the date of first eGFR <60, otherwise, we treated those as censored. The MST was estimated by P/I ratio. Using the prospective cohort, those baseline factors were conducted for risk prediction model estimation. The stepwise method was used for significant variable selection with p-value<0.1 criterion. Both Poisson regression and Cox proportional hazards regression model were employed for risk prediction of CKD incidence, which were reported by adjusted relative risk (aRR) and hazards ratio (aHR) for both respectively after adjustment. Results The overall prevalence rate of CKD complication was 19.62% (18.55%~20.69%) among diabetes patients. The prevalence rate was higher in Men compared with female. For those who have coronary heart disease (CAD), hypertension, abnormal HbA1c level, higher triglyceride (TG) or lower high density lipoprotein (HDL), the prevalence rate were higher than others. The overall incidence rate of CKD was 4.50(2.98~6.02)/per 100. For those who were male, CAD, hypertension, higher TG level, the incidence was higher compared with normal subjects. The overall MST was 5.42 years, which were also 6.41 and 4.52 years for male and female respectively. About the risk factors for CKD prediction, the risk of those patients were being male (aRR=1.35, 95%CI:1.16, 1.56), elders(aRR=2.25, 95%CI:2.17, 2.93), hypertension (aRR=1.62, 95%CI:1.35, 1.94), abnormal HbA1c level (aRR=1.23, 95%CI:1.06, 1.45), lower HDL (aRR=1.36, 95%CI:1.16, 1.61), and higher TG (aRR=1.30, 95%CI:1.12, 1.52)were significantly increased. Both Poisson and Cox regression showed the quite close results. Conclusion According to our result in MST, the appropriate interval for CKD surveillance would be suggested by 2-3 years (half of MST)。Our study also revealed those diabetes patients with male, elder, hypertension, abnormal HbA1c, lower HDL, and higher TG made significantly effect on CKD incidence.
4

Chung, Yi-Ta, and 鍾易達. "A Diabetes Mellitus Complication Prediction Model based on Statistical and Machine Learning Algorithms." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5396017%22.&searchmode=basic.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
碩士
國立中興大學
資訊管理學系所
107
According to IDF, there are more than 425 million people worldwide suffering from diabetes, mostly type 2 diabetes; 12% of global health spending is for diabetes ($727 billion) [1]. According to the 2009 International Diabetes Federation, the prevalence of diabetes is likely to reach 550 million person-times by 2030 [2]. This study sampled the data of foreign physioNet, read the data with Python Pandas, merge and complete the data pre-processing, after eliminating the abnormal value, there are 57,812 patient data, containing multiple different types of diabetes data. Through various machine learning algorithms, including classification trees such as decision trees, neural networks and support vector machines, the accuracy of various analysis methods for diabetes prediction is compared. And find out the comorbid relationship between it and different attributes. After pre-processing, we used 80% of the data as a training data set, 10% as a verification data set, and the remaining 10% as a test data set. Decision tree, MLP neural network, and SVM support vector machine are used for predictive analysis. The study found that patients with both diabetes and three types of diabetes were classified and predicted to have a low accuracy rate, and patients with only one type of diabetes had the best performance. A single type of diabetes has the best performance in MLP classification with an accuracy rate of 0.945. The classification has four types of diabetes with the best MLP and an accuracy rate of 0.9.
5

Santucci, Lara. "Évaluation de nouvelles méthodes de prédiction et de dépistage précoce de l’albuminurie chez les patients avec diabète de type 2." Thesis, 2019. http://hdl.handle.net/1866/24507.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Le diabète de type 2 (DT2) est une maladie chronique grave et sa prévalence ne cesse d’augmenter partout dans le monde. La complication la plus sévère et la plus courante du diabète est la néphropathie diabétique dont le premier symptôme est l’albuminurie. Notre premier objectif est d’évaluer si un dépistage précoce de l’albuminurie permet une meilleure prise en charge de cette complication dans la pratique générale des médecins. Notre deuxième objectif est de valider l’efficacité de notre score de risque polygénique (SRP) sur la prédiction du risque d’albuminurie sur une cohorte canadienne composée de patients DT2, hypertendus provenant de groupe de médecine familiale (GMF) et de family health team (FHT) au Québec et en Ontario (CLINPRADIA I). Le SRP a permis de déterminer les 30% de patients à risque élevé de développer l’albuminurie. En effet, la prévalence d’albuminurie des 30% des sujets classés à haut risque génétique par le SRP était 2,6 fois plus élevée que le reste des patients de CLINPRADIA I. Dans la même cohorte, nous avons démontré que l’introduction d’un point of care testing (POCT) a amélioré la pratique et l’adhésion des médecins aux lignes directrices du traitement de l’albuminurie. Les valeurs d’albuminurie et le nombre de patients albuminuriques ont diminué significativement en réponse à l’introduction du POCT. Nous pouvons conclure de nos résultats que l’utilisation de notre SRP permettrait d’identifier les patients à risque élevés d’albuminurie alors que le POCT permettrait un dépistage précoce et un meilleur suivi de l’albuminurie chez ces patients.
Type 2 diabetes (T2D) is a serious chronic disease and its prevalence keeps increasing all over the world. The most severe and common diabetes complication is nephropathy of which the first symptom is albuminuria. Our first objective is to evaluate if early screening of albuminuria allows for a better patient care of this condition in general practitioner practice. Our second objective is to validate the efficacy of our polygenetic risk score (PRS) on the risk prediction of albuminuria on Canadian cohort composed of hypertensive TD2 patients from groupe de médecine familiale (GMF) and family health team (FHT) in Quebec and in Ontario (CLINPRADIA I). The PRS identified the 30% of T2D patients at high risk of developing albuminuria. Indeed, the albuminuria prevalence of the 30% of subjects at high genetic risk based on the PRS was 2.6 times higher than the remaining patients of CLINPRADIA I. In the same cohort, we established that the introduction of the point of care testing (POCT) improves the practice and the adherence of physicians to the guidelines for the treatment of albuminuria. The values of albuminuria and the number of patients with albuminuria decreased significantly after the introduction of the POCT. We can conclude from our results that the use of our PRS enables the early identification of the patients at high risk of albuminuria while the POCT enables the early detection of patients with albuminuria who benefited from an early intervention.

Books on the topic "Diabetes Complication predictions":

1

Kendrisic, Mirjana, and Borislava Pujic. Endocrine and autoimmune disorders. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713333.003.0047.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Advanced maternal age and increasing numbers of women of childbearing age with endocrine and autoimmune disorders have become the challenge for both anaesthetists and obstetricians. Genetic studies have provided new insight into underlying causes of endocrine disorders and prenatal prediction of inheritance. The expression of endocrine disease may influence the interpretation of diagnostic laboratory testing during pregnancy. Better understanding of the pathophysiological mechanisms enables new therapeutic approaches which can compromise pregnancy outcome. Although only a small number of drugs have been shown through clinical studies to be safe for use in pregnancy, intensive therapy for chronic disease is usually needed. Thus, anaesthetic management of women with endocrine disorders in pregnancy has become more complex. The most frequently encountered endocrine disorders during pregnancy include gestational diabetes mellitus and thyroid and adrenal disorders. Gestational diabetes has become increasingly common in pregnant women. Not only does it influence pregnancy outcome, but it also carries a risk for mother and offspring of developing type 2 diabetes later in life. Intensive glucose control may prevent maternal and fetal complications and improve long-term outcome. Pregnancy itself has been found to influence the course of autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus. However, autoimmune diseases may have adverse consequences for maternal, fetal, and neonatal health. There is a relative paucity of literature concerning anaesthetic management of autoimmune diseases. Early recognition and immediate treatment of the common complications have been the key elements to achieving the ultimate goal—good pregnancy outcome.

Book chapters on the topic "Diabetes Complication predictions":

1

Aragón-Sánchez, Javier, and Rajgopal Mani. "Predicting Wound Healing in the Diabetic Foot: Measuring Skin Viability." In Management of Diabetic Foot Complications, 51–63. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-4525-7_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hinchliffe, Robert J., and Luke Hopkins. "Predicting Wound Healing in the Diabetic Foot: Measuring Tissue Perfusion." In Management of Diabetic Foot Complications, 45–54. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-05832-5_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Mathura Bai, B., N. Mangathayaru, and B. Padmaja Rani. "Diabetes Complications Prediction Using Different Multi-label Classification Algorithms-MEKA." In ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, 386–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8461-5_43.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Tripathi, Abhay Kumar, Sumita Mishra, and Shriram Kris Vasudevan. "Real-Time Prediction of Diabetes Complications Using Regression-Based Machine Learning Models." In Lecture Notes in Networks and Systems, 271–85. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1923-5_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Pavate, Aruna, Pranav Nerurkar, Nazneen Ansari, and Rajesh Bansode. "Early Prediction of Five Major Complications Ascends in Diabetes Mellitus Using Fuzzy Logic." In Soft Computing in Data Analytics, 759–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_72.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Younus, Muhammad, Md Tahsir Ahmed Munna, Mirza Mohtashim Alam, Shaikh Muhammad Allayear, and Sheikh Joly Ferdous Ara. "Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach." In Studies in Big Data, 103–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32587-9_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Munna, Md Tahsir Ahmed, Mirza Mohtashim Alam, Shaikh Muhammad Allayear, Kaushik Sarker, and Sheikh Joly Ferdaus Ara. "Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering." In Advances in Intelligent Systems and Computing, 451–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03402-3_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Munna, Md Tahsir Ahmed, Mirza Mohtashim Alam, Shaikh Muhammad Allayear, Kaushik Sarker, and Sheikh Joly Ferdaus Ara. "Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering." In Lecture Notes in Networks and Systems, 1031–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12388-8_71.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Battula, Vamsi Krishna, P. Satheesh, B. Srinivas, A. Chandra Sekhar, and V. Aswini Sujatha. "Role of Advanced Glycated End Products (AGEs) in Predicting Diabetic Complications Using Machine Learning Tools: A Review from Biological Perspective." In Lecture Notes in Electrical Engineering, 1535–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_138.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sharma, Aman, Archit Kaushal, Kartik Dogra, and Rajni Mohana. "Deep Learning Perspectives for Prediction of Diabetic Foot Ulcers." In Metaverse Applications for Intelligent Healthcare, 203–28. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9823-1.ch006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
A significant complication of diabetes mellitus, diabetic foot ulcers (DFUs), can have devastating repercussions if they are not identified and treated right away. Machine learning algorithms have gained more attention recently for their potential to anticipate DFUs before they manifest, enabling early management and preventing consequences. In this chapter, the authors examine how convolutional neural networks (CNNs) can be used to forecast DFUs. The performance of DenseNet, EfficientNet, and a regular CNN are specifically compared. With labels identifying the presence or absence of a DFU, the authors use a dataset of medical photographs of diabetic feet to train each model. The objective is to assess the effectiveness of these models and look at how each layer affects the precision of the predictions. The authors also hope to provide some light on how the algorithms are able to pinpoint foot regions that are most likely to get DFUs. They also look into how each CNN model's different layers affect prediction accuracy.

Conference papers on the topic "Diabetes Complication predictions":

1

Roy, Mr Mrinmoy, and Dr Mohit Jamwal. "The Advent of Artificial Intelligence in Diabetes Diagnosis: Current Practices and Building Blocks for Future Prospects." In 2nd International Conference on Public Health and Well-being. iConferences (Pvt) Ltd, 2022. http://dx.doi.org/10.32789/publichealth.2021.1008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
India has the highest proportion of diabetes patients, and it is estimated that there will be 134 Million diabetics in India by 2045 as per IDF. Also, the disease burden is increasing to the young population between ages 25-40 as more of them are diagnosed positive according to JAMA recently. Moreover, there are only 4.8 Doctors per 10,000 population, and in villages, the ratio is the lowest possible in this country, according to the Indian Journal of Public Health. Therefore, screening & predicting Diabetes at an early stage remains a priority for clinicians. It reduces the risk of major complications and improves patients' quality of life with diabetes, and builds resilience and well-being amongst other citizens. With the advancement of Computer Science & Artificial Intelligence, it is now possible to predict diabetes and other such diseases through applying deep learning algorithms in high-quality data sets. This helps in a more accurate and faster diagnosis of Pre-diabetes, Diabetes & diabetes-related progressive eye diseases. In this study, a systematic review of the Pubmed repository for current practices to diagnose Diabetes based on AI intervention in the Indian context is carried out. Also, a critical analysis was done on various pioneered companies currently offering AI-based Diabetes diagnostic services in India. The study represents different concepts of AI tools used to predict the diseases currently available in India. Although most of the studies were carried out on Diabetic Retinopathy screening, future opportunities can be in several other areas such as Clinical Decision Support, Predictive Population Risk Stratification and Patient Self-Management Tools.
2

Kantawong, Krittika, Supan Tongphet, Panu Bhrommalee, Napa Rachata, and Sakkayaphop Pravesjit. "The Methodology for Diabetes Complications Prediction Model." In 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON). IEEE, 2020. http://dx.doi.org/10.1109/ectidamtncon48261.2020.9090700.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Albi, Takrim Rahman, Md Nakhla Rafi, Tasfia Anika Bushra, and Dewan Ziaul Karim. "Diabetes Complication Prediction using Deep Learning-Based Analytics." In 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE). IEEE, 2022. http://dx.doi.org/10.1109/icaeee54957.2022.9836401.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Romeo, Luca, Giuseppe Armentano, Antonio Nicolucci, Marco Vespasiani, Giacomo Vespasiani, and Emanuele Frontoni. "A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/593.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
The prediction of the risk profile related to the cardiopathy complication is a core research task that could support clinical decision making. However, the design and implementation of a clinical decision support system based on Electronic Health Record (EHR) temporal data comprise of several challenges. Several single task learning approaches consider the prediction of the risk profile related to a specific diabetes complication (i.e., cardiopathy) independent from other complications. Accordingly, the state-of-the-art multi-task learning (MTL) model encapsulates only the temporal relatedness among the EHR data. However, this assumption might be restricted in the clinical scenario where both spatio-temporal constraints should be taken into account. The aim of this study is the proposal of two different MTL procedures, called spatio-temporal lasso (STL-MTL) and spatio-temporal group lasso (STGL-MTL), which encode the spatio-temporal relatedness using a regularization term and a graph-based approach (i.e., encoding the task relatedness using the structure matrix). Experimental results on a real-world EHR dataset demonstrate the robust performance and the interpretability of the proposed approach.
5

Longato, Enrico, Gian Paolo Fadini, Giovanni Sparacino, Lorenzo Gubian, and Barbara Di Camillo. "Prediction of Cardiovascular Complications in Diabetes from Pharmacy Administrative Claims." In 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON). IEEE, 2020. http://dx.doi.org/10.1109/melecon48756.2020.9140600.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Farzi, Saeed, Sahar Kianian, and Ilnaz Rastkhadive. "Predicting serious diabetic complications using hidden pattern detection." In 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2017. http://dx.doi.org/10.1109/kbei.2017.8324885.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Erandathi, Madurapperumage A., William Y. C. Wang, and Michael Mayo. "Predicting Diabetes Mellitus and its Complications through a Graph-Based Risk Scoring System." In ICMHI 2020: 2020 4th International Conference on Medical and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3418094.3418115.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Pavate, Aruna, and Nazneen Ansari. "Risk Prediction of Disease Complications in Type 2 Diabetes Patients Using Soft Computing Techniques." In 2015 Fifth International Conference on Advances in Computing & Communications (ICACC). IEEE, 2015. http://dx.doi.org/10.1109/icacc.2015.61.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kinsey, Sarah, Jack Wolf, Nalini Saligram, Varun Ramesan, Meeta Walavalkar, Nidhi Jaswal, Sandhya Ramalingam, Arunesh Sinha, and Thanh Nguyen. "Building a Personalized Messaging System for Health Intervention in Underprivileged Regions Using Reinforcement Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/668.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
This work builds an effective AI-based message generation system for diabetes prevention in rural areas, where the diabetes rate has been increasing at an alarming rate. The messages contain information about diabetes causes and complications and the impact of nutrition and fitness on preventing diabetes. We propose to apply reinforcement learning (RL) to optimize our message selection policy over time, tailoring our messages to align with each individual participant's needs and preferences. We conduct an extensive field study in a large country in Asia which involves more than 1000 participants who are local villagers and they receive messages generated by our system, over a period of six months. Our analysis shows that with the use of AI, we can deliver significant improvements in the participants' diabetes-related knowledge, physical activity levels, and high-fat food avoidance, when compared to a static message set. Furthermore, we build a new neural network based behavior model to predict behavior changes of participants, trained on data collected during our study. By exploiting underlying characteristics of health-related behavior, we manage to significantly improve the prediction accuracy of our model compared to baselines.
10

Muharram, Arief Purnama, and Fahmi Sajid. "Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus." In 2023 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2023. http://dx.doi.org/10.1109/iceei59426.2023.10346215.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Diabetes Complication predictions":

1

Ferdosian, Hengameh, Hadi Zamanian, Sayed Ali Emami, Elahe Sedighi, Mina Moridi, and Maryam Doustmehraban. Application of artificial intelligence in prediction of cardiovascular complications in patients with diabetes mellitus type 2: A protocol of systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2021. http://dx.doi.org/10.37766/inplasy2021.10.0076.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Review question / Objective: The aim of this systematic review is to evaluate AI-based models in identifying predictors of cardiovascular events and risk predtion in patients with diabetes mellitus type2. Condition being studied: T2DM patients have an increased risk of macrovascular and microvascular complications, lead to decreased quality of life and mortality. Considering the significance of cardiovascular complications in these patients, prediction of such events would be important. Different traditional statistical methods(such as regression) and new AI-besed algorithms are used to predict these complications in diabetic patients.
2

Gupta, Shikhar, Mehtab Ahmed, Sayema ., Azam Haseen, and Saif Quaiser. Relevance of Preoperative Vessel Mapping and Early Postoperative Ultrasonography in Predicting AV Fistula Failure in Chronic Kidney Disease Patients. Science Repository, February 2024. http://dx.doi.org/10.31487/j.rdi.2023.02.02.

Full text
APA, Harvard, Vancouver, ISO, and other styles
Abstract:
Introduction: The increasing prevalence of chronic kidney disease (CKD), coupled with advancements in the diagnosis and treatment of renal diseases and improvements in life expectancy, has led to a greater number of patients requiring hemodialysis. The preferred method of vascular access for hemodialysis is AV fistula formation; however, it is associated with a high rate of failure. In our prospective study, we focused on 40 CKD patients planned for initiation of maintenance hemodialysis. Methods: We employed preoperative ultrasound mapping to assess cephalic vein diameter, compressibility, and colour flow, as well as radial and brachial artery diameter, peak systolic velocity, and intimal wall calcification. Postoperatively, ultrasound examinations were conducted on day 7 and at 6 weeks to evaluate fistula blood volume and detect any complications. Results: A significant association between fistula failure and cephalic vein diameter, brachial artery diameter, intimal vessel wall calcification, and comorbid conditions like diabetes mellitus was observed. Furthermore, blood flow at day 7 was notably lower in the failure group compared to those with a functioning fistula and any fistula with blood flow <154 ml/min on day 7 may be predictive of early fistula failure. Conclusion: Preoperative vessel mapping and early postoperative ultrasonography is indispensable for patients who require AV fistula formation for hemodialysis and provide valuable information for selecting suitable vessels for successful fistula creation and enable early intervention to salvage a failing fistula after the surgery. By utilizing these, healthcare professionals can make informed decisions and take necessary steps to optimize the outcomes of AV fistula formation in patients undergoing hemodialysis.

To the bibliography