Heterogeneous Methodology to Support the Early Diagnosis of Gestational Diabetes

Filho, Egidio Gomes; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calí­ope Dantas; Nunes, Luciano Comin; Gomes, Luiza Barcelos Gualberto

doi:10.1109/access.2019.2903691

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2020.3028333, IEEE Access specialized system is proposed in [35]: BN, AI and Multiplecriteria decision analysis (MCDA). Agent-Oriented Modelling (AOM) was reported in [57] to create a scalable multi-agent architecture for environments with limited connectivity.…”

Section: Methodology and Knowledge Representationmentioning

confidence: 99%

“…A 44.4% (n = 8) of studies from this category collected data from medical records. In 22.2% (n = 4) of studies sensors Early diagnosis of GDM [35] were employed for data acquisition (BP measurements), and data from human input was collected in 11.1% (n = 2) of studies, via maternal questionnaires and mobile phone usage. 16.6% (n = 3) of studies used survey data and 11.1% (n = 2) of studies used medical equipment.…”

Section: G Pre-eclampsia and Hypertensive Disordersmentioning

confidence: 99%

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Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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Artificial Intelligence has been widely applied to a majority of research areas, including health and medicine. Certain complications or disorders that can appear during pregnancy can endanger the life of both mother and fetus. There is enough scientific literature to support the idea that emotional aspects can be a relevant risk factor in pregnancy (such as anxiety, stress or depression, for instance). This paper presents a scoping review of the scientific literature from the past 12 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) to identify which methodologies, techniques, algorithms and frameworks are used in Artificial Intelligence and Affective Computing for pregnancy health and well-being. The methodology proposed by Arksey and O'Malley, in conjunction with PRISMA-ScR framework has been used to create this review. Despite the relevance that emotional status can have as a risk factor during pregnancy, one of the main findings of this study is that there is still not a significant amount of literature on automatic analysis of emotion. Health enhancement and well-being for pregnant women can be achieved with artificial intelligence or affective computing based devices, hence future work on this topic is strongly suggested.

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Section: Methodology and Knowledge Representationmentioning

confidence: 99%

Section: G Pre-eclampsia and Hypertensive Disordersmentioning

confidence: 99%

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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“…Pregnancy causes the body to go through significant changes, and unfortunately there are several maternal complications that can occur during this time. Our query found papers focused on gestational diabetes mellitus (GDM) [ 23 , 24 , 30 , 33 – 36 , 41 ], gestational hypertension disorders [ 21 , 25 , 26 ], and bacteriuria [ 31 ]. Studies aim to predict and classify disease in early pregnancy, improve screenings, and provide clinical decision support for disease management.…”

Section: Discussionmentioning

confidence: 99%

“…There have been efforts to diagnose GDM earlier in pregnancy, when current medical protocols using Oral Glucose Tolerance Test are not feasible. Filho et al propose a hybrid methodology in order to support the early diagnosis of GDM [24]. The method combines aspects of BNs, Multicriteria Analysis and Expert Systems.…”

Section: Pregnancy-induced Diseasementioning

confidence: 99%

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Davidson

Boland

2020

J Pharmacokinet Pharmacodyn

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The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and SCOPUS. Search terms included pregnancy and AI. Research articles and book chapters were included, while conference papers, editorials and notes were excluded from the review. Included papers focused on pregnancy and AI methods, and pertained to pharmacologic interventions. We identified 376 distinct studies from our queries. A final set of 31 papers were included for the review. Included papers represented a variety of pregnancy concerns and multidisciplinary applications of AI. Few studies relate to pregnancy, AI, and pharmacologics and therefore, we review carefully those studies. External validation of models and techniques described in the studies is limited, impeding on generalizability of the studies. Our review describes how AI has been applied to address maternal health, throughout the pregnancy process: preconception, prenatal, perinatal, and postnatal health concerns. However, there is a lack of research applying AI methods to understand how pharmacologic treatments affect pregnancy. We identify three areas where AI methods could be used to improve our understanding of pharmacological effects of pregnancy, including: (a) obtaining sound and reliable data from clinical records (15 studies), (b) designing optimized animal experiments to validate specific hypotheses (1 study) to (c) implementing decision support systems that inform decision-making (11 studies). The largest literature gap that we identified is with regards to using AI methods to optimize translational studies between animals and humans for pregnancyrelated drug exposures.

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“…For the most part, it happens at 40 years of age [ 8 ]. Next, gestational diabetes mellitus (GDM) [ 9 ] is associated with adverse perinatal outcomes [ 10 ]. The last one is pregestational diabetes [ 11 ] that is diagnosed before pregnancy.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

Annamalai¹,

Nedunchelian

2021

Computational Intelligence and Neuroscience

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Today, diabetes is one of the most prevalent, chronic, and deadly diseases in the world owing to some complications. If accurate early diagnosis is feasible, the risk factor and incidence of diabetes may be greatly decreased. Diabetes prediction is stable and reliable, since there are only minimal labelling evidence and outliers found in the datasets of diabetes. Numerous works coped with diabetes disease prediction and provided the solution. But the existing methods proffered low accuracy detection and consumed more training time. So, this paper proposed an OWDANN algorithm for diabetes mellitus disease prediction and severity level estimation. The proposed system mainly consists of two phases, namely, disease prediction and severity level estimation phase. In the disease prediction phase, the preprocessing is performed for the Pima dataset. Then, the features are extracted from the preprocessed data, and finally, the classification step is performed by using OWDANN. In the severity level estimation phase, the diabetes positive dataset is preprocessed first. Then, the features are extracted, and lastly, the severity level is predicted using GDHC. The extensive experimental results showed that the proposed system outperforms with 98.97% accuracy, 94.98% sensitivity, 95.62% specificity, 97.02% precision, 93.84% recall, 9404% f-measure, 0.094% FDR, and 0.023% FPR compared with the state-of-the-art methods.

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Heterogeneous Methodology to Support the Early Diagnosis of Gestational Diabetes

Cited by 13 publications

References 10 publications

Artificial Intelligence in Pregnancy: A Scoping Review

Artificial Intelligence in Pregnancy: A Scoping Review

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm

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