Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates

Galderisi, Alfonso; Zammataro, Luca; Losiouk, Eleonora; Lanzola, Giordano; Kraemer, Kristen; Facchinetti, Andrea; Galeazzo, Beatrice; Favero, Valentina; Baraldi, Eugenio; Cobelli, Claudio; Trevisanuto, Daniele; Steil, Garry M.

doi:10.1089/dia.2018.0383

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…AI-biosensors are optimally connected via wireless communication to transfer information between the biosensors and smartphone platforms or other intelligent devices. Popular wireless technologies used in AI-biosensor networks (AIBN) include Bluetooth, radio-frequency identification (RFID), near-field communication (NFC), Wi-Fi, and ZigBee (Galderisi et al, 2019).…”

Section: Wireless Communicationmentioning

confidence: 99%

Advancements in artificial intelligence and machine learning in revolutionising biomarker discovery

Raikar,

Somnache

2023

Braz. J. Pharm. Sci.

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The article explores the significance of biomarkers in clinical research and the advantages of utilizing artificial intelligence (AI) and machine learning (ML) in the discovery process. Biomarkers provide a more comprehensive understanding of disease progression and response to therapy compared to traditional indicators. AI and ML offer a new approach to biomarker discovery, leveraging large amounts of data to identify patterns and optimize existing biomarkers. Additionally, the article touches on the emergence of digital biomarkers, which use technology to assess an individual's physiological and behavioural states, and the importance of properly processing omics and multi-omics data for efficient handling by computer systems. However, the article acknowledges the challenges posed by AI/ML in the identification of biomarkers, including potential biases in the data and the need for diversity in data representation. To address these challenges, the article suggests the importance of regulation and diversity in the development of AI/ML algorithms.

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Section: Wireless Communicationmentioning

confidence: 99%

Advancements in artificial intelligence and machine learning in revolutionising biomarker discovery

Raikar,

Somnache

2023

Braz. J. Pharm. Sci.

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“…Through the analysis of big data, it is possible to give early warning of possible Internet financial risks, so that relevant enterprise personnel can formulate reasonable preventive strategies in time, and ensure the normal operation of Internet finance through the analysis of multiple data. AI, as a product of the information age, provides great convenience for people [5]. With the continuous development and wide application of AI, it is necessary to speed up the upgrading of traditional industries, especially relative to the financial industry.…”

Section: Introductionmentioning

confidence: 99%

Research on Financial Risk Assessment Based on Artificial Intelligence

Zhao

2022

SHS Web Conf.

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The effective application of artificial intelligence (AI) models in various fields in the field of financial risk can improve the speed of data processing, deepen the degree of data analysis and reduce the labor cost, thus effectively improving the efficiency of financial risk control. The application of AI in the field of financial risk management puts forward new requirements for the system setup and operation mode of financial supervision. With the rapid growth ofcomputer and network technology, the increase of market transaction frequency, the diversification of data sources and the development and application of big data, it brings new challenges to financial risk management based on massive data. Based on this, this paper analyzes the role of AI in promoting the reform and growth ofthe financial industry, and puts forward some countermeasures for rational use of AI in the field of financial risk management.

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“…The safety of continuous glucose monitoring (CGM) in preterm neonates has been largely explored, as well as the association of hyperglycemia with clinically relevant adverse neurological outcomes [ 4 – 10 ], thus suggesting a new tool to continuously monitor glucose changes in this population and the effect of neonatal care on glucose fluctuation, as a surrogate marker of neonatal stress.…”

Section: Introductionmentioning

confidence: 99%

Glucose variability increases during minimally invasive procedures in very preterm infants

et al. 2022

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The objective of this study is to assess the effect of neonatal procedures on glucose variability in very preterm infants. Preterm infants (≤ 32 weeks gestation and/or birthweight ≤ 1500 g) were started on continuous glucose monitoring (CGM) on day 2 of birth and monitored for 5 days. Minimally invasive (heel stick, venipunctures) and non-invasive (nappy change, parental presence) procedures were recorded. CGM data were analyzed 30 min before and after each procedure. The primary outcome was the coefficient of glucose variation (CV = SD/mean) before and after the procedure; SD and median glucose were also evaluated. We analyzed 496 procedures in 22 neonates (GA 30.5 weeks [29–31]; birthweight 1300 g [950–1476]). Median glucose did not change before and after each procedure, while CV and SD increased after heel prick (p = 0.017 and 0.030), venipuncture (p = 0.010 and 0.030), and nappy change (p < 0.001 and < 0.001), in the absence of a difference during parental presence.Conclusions: Non-invasive and minimally invasive procedures increase glucose variability in the absence of changes of mean glucose. What is Known:• Minimally invasive procedures - including nappy change - may increase neonatal stress in preterm infants. What is New:• Continuous glucose monitoring provides a quantitative measure of neonatal stress during neonatal care procedures demonstrating an increase of glucose variability.

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Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates

Cited by 8 publications

References 34 publications

Advancements in artificial intelligence and machine learning in revolutionising biomarker discovery

Advancements in artificial intelligence and machine learning in revolutionising biomarker discovery

Research on Financial Risk Assessment Based on Artificial Intelligence

Glucose variability increases during minimally invasive procedures in very preterm infants

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