Remote Monitoring System for the Detection of Prenatal Risk in a Pregnant Woman

Veena, S.; Aravindhar, D. John

doi:10.1007/s11277-021-08249-x

Cited by 18 publications

(7 citation statements)

References 18 publications

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“…The peak in the number of publications was reached in 2021, and the fields that AI has been applied to are currently more diverse. For example, Veena and Aravindhar (2021) used machine learning techniques to design a remote monitoring system for prenatal risk detection that allows healthcare providers to connect with pregnant women for remote supervision and consultation effectiveness. Moreover, Moreno-Fergusson et al (2021) illustrated the use of an AI approach to analyse healthcare data; the generated rules were then employed in nurse care management for inpatients.…”

Section: Resultsmentioning

confidence: 99%

Trends in artificial intelligence in nursing: Impacts on nursing management

Chang

Jen

2022

J Nursing Management

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Objective To investigate the academic use of artificial intelligence (AI) in nursing. Background A bibliometric analysis combined with the VOSviewer software quantification method has been utilized for a literature analysis. In recent years, this approach has attracted the interest of scholars in various research fields. Thus far, there is no publication using bibliometric analysis combined with the VOSviewer software to analyse the applications of AI in nursing. Method A bibliometric analysis methodology was used to search for relevant articles published between 1984 and March 2022. Six databases, Embase, Scopus, PubMed, CINAHL, WoS and MEDLINE, were included to identify relevant studies, and data such as the year of publication, journals, country, institutional source, field and keywords were analysed. Results Most relevant articles were published from institutions in the United States. The League of European Research Universities has published most research studies that use AI and nursing. Scholars have mainly focused on nursing, medical informatics, computer science AI, healthcare sciences services and physics particles fields. Commonly used keywords were machine learning, care, AI, natural language processing, prediction and nurse. Conclusion Research articles were mainly published in Nurse Education Today. Research topics such as AI‐assisted medical recording and medical decision making were also identified. According to this study, AI in nursing has the potential to attract more attention from researchers and nursing managers. Additional high‐quality research beyond the scope of medical education, as well as on cross‐domain collaboration, is warranted to explore the acceptability and effective implementation of AI technologies. Implications for Nursing Management This study provides scholars and nursing managers with structured information regarding the use of AI in nursing based on scientific and technological developments across different fields and institutions. The application of AI can improve nursing management, nursing quality, safety management and team communication, as well as encourage future international collaboration.

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Section: Resultsmentioning

confidence: 99%

Trends in artificial intelligence in nursing: Impacts on nursing management

Chang

Jen

2022

J Nursing Management

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“…Veena et al [13] developed a system for the timely supervision of a pregnant women's health status using wireless sensors. The architecture is cloud-based and uses sensor networks, mobile devices, and instant communication through cellular networks.…”

Section: Related Workmentioning

confidence: 99%

Hacia embarazos más seguros

Nielsen Pimentel,

Villarreal,

Muñoz Arracera

et al. 2023

ing. Solidar

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Introduction: Hypertensive disorders in pregnancy pose global health challenges. Maternal mortality rates in Panama due to preeclampsia and eclampsia have increased. A mobile application is introduced to monitor and control these disorders. Problem: Hypertensive disorders in pregnancy pose a risk to maternal health. Panama’s healthcare initiatives have shown progress, but there are still gaps in maternal care. Novel technologies may help address these gaps. Objective: The goal is to evaluate a prototype mobile app for managing hypertensive disorders in pregnancy. Research focuses on usability, including navigation, design-task correlation, acceptability, and user perspectives. Methodology: The Design Science Research Methodology (DSRM) guides the study through problem identification, motivation, and software artifact development. The evaluation involves 32 participants engaging in tasks and standardized questionnaires, including the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Tasks assess the app’s usability, while questionnaires provide comprehensive insights into user experiences. Results: Users generally have positive interactions and favorable perceptions. However, there are challenges with task completion, particularly with interface intuitiveness. Conclusion: The app for monitoring hypertensive disorders has shown positive user experiences and usability. However, there are challenges and user feedback that need to be addressed for refinement and effectiveness in supporting maternal health during pregnancy. Limitations: The study focuses only on the prototype evaluation phase and may need further iterations to address challenges. The participant pool’s limitations may impact generalizability. Ongoing improvements are crucial to meet evolving user needs and technological advancements. Originality: This article contributes originality by presenting a novel mobile application prototype for managing hypertensive disorders in pregnancy. The use of DSRM for development and comprehensive usability assessments with standardized tools adds to the originality, providing valuable insights for future advancements in maternal healthcare technology.

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“…The immense growth of ML algorithms to monitor mother and infant health in the earlier stages of pregnancy may help doctors to tackle complications. In the present era, ML approaches are being used for the prediction of preterm birth risk [24], detection of wild stress [25], prenatal risk [26], postpartum depression [27], and congenital heart disease [28] among pregnant women. In addition, the ML approaches have potential to predict the infant's health status and to monitor the brain and general growth of baby.…”

Section: Introductionmentioning

confidence: 99%

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Ahmad

Iqbal

Javaid

et al. 2022

Sensors

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Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers.

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Remote Monitoring System for the Detection of Prenatal Risk in a Pregnant Woman

Cited by 18 publications

References 18 publications

Trends in artificial intelligence in nursing: Impacts on nursing management

Trends in artificial intelligence in nursing: Impacts on nursing management

Hacia embarazos más seguros

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

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