Prediction based health monitoring in pregnant women

Lakshmi, Baddireddi Subhadra; Indumathi, T. S.; Ravi, Nandini

doi:10.1109/icatcct.2015.7456954

Cited by 8 publications

(3 citation statements)

References 1 publication

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“…• Providing maternal, prenatal, antenatal or infant care (n = 10): [48], [148]- [156]. • Health monitoring (n = 5): [148], [151], [155], [157], [158], from which specifically home monitoring [151], [158]. • Evaluation of health programs [159] (exploratory protocol), reducing health care variability [160] (IDSS) and health resources allocation [158] (wearable).…”

Section: K Maternal and Fetus Well-beingmentioning

confidence: 99%

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: K Maternal and Fetus Well-beingmentioning

confidence: 99%

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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“…B.N Lakshmi et al worked out a learning model using C4.5 decision tree algorithm for classification as well as predicting risks induced during pregnancy [13]. The application proposed by the patent using neural networks identifies important input variables for a medical diagnostic test which was used in training the decision-support systems to guide the development of the tests and helps in accessing the effectiveness of a selected therapeutic protocol [14].…”

Section: Related Workmentioning

confidence: 99%

Contextual Care Protocol using Neural Networks and Decision Trees

Sinha

Malviya

Panda

et al. 2018

2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid model leverages neural networks and decision trees. The neural network estimates the chances of each disease and each tree in the decision trees represents care protocol for a disease. These trees are subject to change in case of aberrations found by the diagnosticians. These corrections or prediction errors are clustered into similar groups for scalability and review by the experts. The corrections as suggested by the experts are incorporated into the model.

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“…V.Srinivasan, et al [2] proposed the Fast Classification Algorithm (FCA), hybrid the advantages of ID3 and the SVM, for classification accuracy and to obtain fast result. The disadvantage of id3 not suited for the large database and performance is high in the form of small data base taking this into consideration and used this algorithm for small data where the complex problem lies between the upper and lower area where accurate classification is made based on analyzing the each attribute.…”

Section: Related Workmentioning

confidence: 99%

Women Health Monitoring: A Survey

Shegokar¹,

Paswan²

2017

International Journal of Advanced Research in Computer and Comm

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Abstract:In medical area, various problem exist but gestation stage serious problem regarding women. Women undergo several physiological parameter changes which cause major problem. When we neglect such problem may result in complications or mortality of mother and fetus, so we need to protect pregnant women. In this paper we have discussed a few approaches for find risk during pregnancy and information regarding health care.

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Prediction based health monitoring in pregnant women

Cited by 8 publications

References 1 publication

Artificial Intelligence in Pregnancy: A Scoping Review

Artificial Intelligence in Pregnancy: A Scoping Review

Contextual Care Protocol using Neural Networks and Decision Trees

Women Health Monitoring: A Survey

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