Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications
D Mennickent, A Rodríguez, MC Opazo… - Frontiers in …, 2023 - frontiersin.org
Introduction Machine learning (ML) corresponds to a wide variety of methods that use
mathematics, statistics and computational science to learn from multiple variables …
mathematics, statistics and computational science to learn from multiple variables …
A systematic review and meta-analysis of digital application use in clinical research in pain medicine
Importance Pain is a silent global epidemic impacting approximately a third of the
population. Pharmacological and surgical interventions are primary modes of treatment …
population. Pharmacological and surgical interventions are primary modes of treatment …
Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases
Background Despite technological advancement in the field of healthcare, the worldwide
burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a …
burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a …
[HTML][HTML] A medical cyber-physical system for predicting maternal health in developing countries using machine learning
MM Hossain, MA Kashem, NM Nayan… - Healthcare …, 2024 - Elsevier
It is essential to monitor any health issues during pregnancy to ensure a safe delivery
because pregnancy is crucial for both mother and child. However, developing countries …
because pregnancy is crucial for both mother and child. However, developing countries …
A research study on the cervical cerclage to deal with cervical insufficiency using machine learning
The modern era is responsible for reducing the fertility rate is in women due to many
evolving factors such as stress, work pressure, sedentary lifestyle. This research article is …
evolving factors such as stress, work pressure, sedentary lifestyle. This research article is …
Systematic reviews of machine learning in healthcare: a literature review
K Kolasa, B Admassu… - Expert Review of …, 2024 - Taylor & Francis
Introduction The increasing availability of data and computing power has made machine
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …
A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques
Abstract Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological
abnormalities seen in reproductive female bodies posing serious health hazards. The …
abnormalities seen in reproductive female bodies posing serious health hazards. The …
Advancements and Challenges in Non-Invasive Electrocardiography for Prenatal, Intrapartum, and Postnatal Care: A Comprehensive Review
RA Marnani, R Jaros, J Pavlicek, R Martinek… - IEEE …, 2024 - ieeexplore.ieee.org
Non-invasive electrocardiography (NI-ECG) has become an indispensable tool for
monitoring fetal and neonatal cardiac activity throughout the stages of pregnancy and …
monitoring fetal and neonatal cardiac activity throughout the stages of pregnancy and …
Automatic fetal ultrasound image segmentation of first trimester for measuring biometric parameters based on deep learning
L Liu, D Tang, X Li, Y Ouyang - Multimedia Tools and Applications, 2024 - Springer
Transvaginal ultrasonography (TVS) is a common method used by doctors to monitor the
embryonic development. In the early stage of pregnancy, doctors assess the growth and …
embryonic development. In the early stage of pregnancy, doctors assess the growth and …
A novel strategy to classify chronic patients at risk: a hybrid machine learning approach
Various care processes have been affected by COVID-19. One of the most dramatic has
been the care of chronic patients under medical supervision. According to the World Health …
been the care of chronic patients under medical supervision. According to the World Health …