AIMC Topic: Machine Learning

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Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis.

BMC public health
INTRODUCTION: Maternal mortality refers to a mother's death owing to complications arising from childbirth or pregnancy. This issue is a forefront public health challenge around the globe which is pronounced in low- and middle-income countries, parti...

Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses.

BMC medical informatics and decision making
INTRODUCTION: Cellular Communication Network Factor 6 (CCN6) is an adipokine whose production undergoes significant alterations in metabolic disorders. Given the well-established link between obesity-induced adipokine dysfunction and the development ...

Development and validation of a machine learning model for predicting intrapartum fever using pre-labor analgesia clinical indicators: a multicenter retrospective study.

BMC pregnancy and childbirth
BACKGROUND: Labor anesthesia is commonly used for pain relief during labor, but it can increase the risk of intrapartum fever. Currently, there are no reliable tools to predict which parturients might develop fever before labor anesthesia. The predic...

Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques.

Scientific reports
Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is benefic...

Proposing a machine learning-based model for predicting nonreassuring fetal heart.

Scientific reports
The capacity to forecast nonreassuring fetal heart (NFH) is essential for minimizing perinatal complications; therefore, this research aims to establish if a machine learning (ML) model can predict NFH. This was a retrospective analysis of informatio...

Hyperspectral discrimination of vegetable crops grown under organic and conventional cultivation practices: a machine learning approach.

Scientific reports
A verifiable and regional level method for mapping crops cultivated under organic practices holds significant promise for certifying and ensuring the quality of farm products marketed as organic. The prevailing method for the identification of organi...

The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review.

JMIR medical informatics
BACKGROUND: Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to b...

Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning.

IEEE journal of biomedical and health informatics
Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To addres...

Property-Guided Few-Shot Learning for Molecular Property Prediction With Dual-View Encoder and Relation Graph Learning Network.

IEEE journal of biomedical and health informatics
Molecular property prediction is an important task in drug discovery. However, experimental data for many drug molecules are limited, especially for novel molecular structures or rare diseases which affect the accuracy of many deep learning methods t...

DRGCL: Drug Repositioning via Semantic-Enriched Graph Contrastive Learning.

IEEE journal of biomedical and health informatics
Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted...