Artificial intelligence: predictive analytics of perinatal risks

Ivshin, A. A.; Гусев, A. В.; Novitskiy, R.E.; Llc, Petrozavodsk K-SkAI

doi:10.20953/1726-1678-2020-6-133-144

Cited by 4 publications

(1 citation statement)

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“…Complications during childbirth are one of the main causes of perinatal mortality [ 36 , 37 , 38 ]. A fetal cardiotocogram (CTG) can be used as a monitoring tool to identify high-risk women during childbirth [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Velichko

2021

Sensors

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Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

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Section: Resultsmentioning

confidence: 99%

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Velichko

2021

Sensors

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Artificial intelligence technologies in predicting preeclampsia

Ivshin

Bagaudin

Гусев³

2021

Akušerstvo, ginekologiâ i reprodukciâ

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Russian Federation's strategy for the conservation of reproductive potential is focused on the personalized health care for women and is based on the preclinical detection of gynecological diseases and major obstetric syndromes at the prediction of adverse outcomes and subsequent preventive measures that can reduce maternal and perinatal morbidity and mortality, decrease women and newborn disability and significantly reduce the extremely high costs of care of the premature infants. The search for effective methods of prediction of preeclampsia at the stage of preconception and in the first trimester of pregnancy is driven by the desire to identify women who have a greater risk of developing hypertensive disorders in order to take the necessary effective measures to prevent placental pathology as early as possible and thus prevent or reduce the rate of preeclampsia. At the same time, identification of women in the high-risk group will allow to plan a rational prenatal care, timely recognize the occurrence of multiple organ dysfunction and immediately begin pathogenetic and symptomatic therapy. Taking into account the national and world experience in the use of predictive analytics software, that has proven the success of these methods in reproductive medicine, it is reasonable to conclude that the conversion of prognosis into a digital format using artificial intelligence algorithms will open new possibilities of increasing the accuracy of individual risk calculation for preeclampsia, corresponding to modern trends of personalized preventive medicine. This scientific review of the Russian and international publications aims to inform a wide group of obstetrician-gynecologists on the achievements of artificial intelligence technologies and the prospects for machine learning in the prediction of preeclampsia.

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Areas for Artificial Intelligence Implementation in Kuzbass Healthcare: Sociological Aspects

Golovko

Kharlampenkov

2023

Bulletin of Kemerovo State University. Series: Political, Sociological and Economic Sciences

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The article analyzes the structure of morbidity in the region and identifies the main directions for artificial intelligence implementation in Russia. In order to identify the attitude of clinical physicians towards the artificial intelligence products, the authors performed a sociological survey. To develop the artificial intelligence in the Kemerovo Region it is necessary to use artificial intelligence products and build competence centers for implementing these products in regional healthcare. The main ways of development are strategic programs; creative teams within scientific and educational centers; introduction of automated workplaces for doctors. The authors’ proposals can improve the accuracy of diagnosis, simplify the treatment of patients with various diseases, and rise the healthcare of the Kemerovo region – Kuzbass to a new level.

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Artificial intelligence: predictive analytics of perinatal risks

Cited by 4 publications

References 0 publications

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Artificial intelligence technologies in predicting preeclampsia

Areas for Artificial Intelligence Implementation in Kuzbass Healthcare: Sociological Aspects

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