Standardising the assessment of caesarean birth using an oxford caesarean prediction score for mothers with gestational diabetes

Pisani, Luigi; Hirst, Jane E; Mackillop, Lucy; Clifton, David A.

doi:10.1049/htl2.12022

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…On top of having a higher risk of having LGA offspring, excessive gestational weight gain is also associated with poorer pregnancy outcomes [6]. Lu et al [93] developed machine learning models on 97 patients with GDM to demonstrate a proof-of-the-concept work of caesarean section prediction and to explore the role of temporal blood glucose in predicting caesarean birth. Logistic regression, SVM and Boosting trees were used in model development.…”

Section: B Medication and Pregnancy Outcome Managementmentioning

confidence: 99%

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes

Pisani

Ding

Hirst

et al. 2024

IEEE Rev. Biomed. Eng.

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Innovations in digital health and machine learning are changing the path of clinical health and care. People from many different geographies and cultures can benefit from the mobility of wearable devices and smartphones to monitor their health in a ubiquitous manner. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes ̶ a subtype of diabetes that occurs during pregnancy. Despite a large number of patients with gestational diabetes, only a handful of digital health applications have been deployed in clinical practice. This paper reviews sensor technologies in blood glucose monitoring devices and machine learning fused digital health innovations for gestational diabetes monitoring and management in both clinical and commercial settings. It is one of the first comprehensive reviews in this area to the best of our knowledge. In conclusion, there is a need to (1) develop digital health technologies and clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment monitoring and planning; (2) adapt and develop clinically proven devices for patient self-management of health and well-being at the hospital and home settings thereby facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for women everywhere.

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Section: B Medication and Pregnancy Outcome Managementmentioning

confidence: 99%

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes

Pisani

Ding

Hirst

et al. 2024

IEEE Rev. Biomed. Eng.

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“…This suppression produces changes in serum cytokines (such as IL‐6, IL‐8, IL‐10, TNF‐α, and IFN‐γ), immune checkpoints (such as PD‐L, PD‐L2, TIM3, LAG3, CTLA4, IDO, CD28, and CD80), and immune cells (such as CD8, Th1/Th2 MDSC, macrophage, platelets, regulatory T cells, and neutrophils) (Dong et al, 2021; Qin et al, 2020). Furthermore, icaritin decreases the levels of tumor‐associated splenic extramedullary hematopoiesis (EMH), resulting in the activation and accumulation of myeloid‐derived suppressor cells (MDSCs) and the recovery of effector T cells function (Lu et al, 2022). In murine HCC models, icaritin decreased tumor‐associated splenic EMH, reducing the generation and activation of MDSC in HCC (Tao et al, 2021).…”

Section: Mechanisms Of Antitumor Action Of Icaritinmentioning

confidence: 99%

New insights into the anticancer therapeutic potential of icaritin and its synthetic derivatives

Reyes‐Hernández,

Figueroa‐González,

Quintas‐Granados

et al. 2024

Drug Development Research

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Icaritin is a natural prenylated flavonoid derived from the Chinese herb Epimedium. The compound has shown antitumor effects in various cancers, especially hepatocellular carcinoma (HCC). Icaritin exerts its anticancer activity by modulating multiple signaling pathways, such as IL‐6/JAK/STAT3, ER‐α36, and NF‐κB, affecting the tumor microenvironment and immune system. Several clinical trials have evaluated the safety and efficacy of icaritin in advanced HCC patients with poor prognoses, who are unsuitable for conventional therapies. The results have demonstrated that icaritin can improve survival, delay progression, and produce clinical benefits in these patients, with a favorable safety profile and minimal adverse events. Moreover, icaritin can enhance the antitumor immune response by regulating the function and phenotype of various immune cells, such as CD8+ T cells, MDSCs, neutrophils, and macrophages. These findings suggest that icaritin is a promising candidate for immunotherapy in HCC and other cancers. However, further studies are needed to elucidate the molecular mechanisms and optimal dosing regimens of icaritin and its potential synergistic effects with other agents. Therefore, this comprehensive review of the scientific literature aims to summarize advances in the knowledge of icaritin in preclinical and clinical studies as well as the pharmacokinetic, metabolism, toxicity, and mechanisms action to recognize the main challenge, gaps, and opportunities to develop a medication that cancer patients can use. Thus, our main objective was to clarify the current state of icaritin for use as an anticancer drug.

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“…The standard scaler is one of the most commonly employed types of feature scaling methods for distance-based learners such as KNN, and SVM [41], [42], [43], [44]. Standard scaler improves the performance of distance based learning algorithm [45], [46], [47], [48], [49].…”

Section: B Data Preprocessingmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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Standardising the assessment of caesarean birth using an oxford caesarean prediction score for mothers with gestational diabetes

Cited by 5 publications

References 14 publications

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes

New insights into the anticancer therapeutic potential of icaritin and its synthetic derivatives

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

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