AIMC Topic: Machine Learning

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Newborn Pulse-Oximetry Screening.

Clinics in perinatology
Pulse oximetry screening (POS) is a noninvasive tool for the detection of critical congenital heart defects (CCHD) that has moderate sensitivity and high specificity. It is readily accepted by parents and health care professional and has significantl...

Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.

Scientific reports
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...

Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.

Scientific reports
Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inher...

Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.

Scientific reports
Low birthweight (LBW) is a significant health challenge worldwide, as these neonates experience both short- and long-term disabilities. Factors affecting maternal and fetal health during early to mid-pregnancy can greatly influence fetal development....

Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning.

Scientific reports
Soil water retention is a critical aspect of water conservation. To quantitatively assess the Soil Water-Holding Capacity (SWHC), this study focused on a typical wild fruit forest in Xinjiang, China. The spectral characteristics of the forest canopy ...

Plasma proteomics for biomarker discovery in childhood tuberculosis.

Nature communications
Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, thus a global priority is the identificati...

Unlocking urban soil secrets: machine learning and spectrometry in Berlin's heavy metal pollution study considering spatial data.

Environmental monitoring and assessment
Berlin has historically been impacted by heavy metal (HM) emissions, raising concerns about soil pollution. In this study, machine learning (ML) techniques were applied to predict HM concentrations across the Berlin metropolitan area. A dataset of 66...

Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.

Nature communications
The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the yields of most ncAA-containing protein  remain low due to the limited activity of PylRS variants. Here, we apply machine...

Integration of label-free surface enhanced Raman spectroscopy (SERS) of extracellular vesicles (EVs) with Raman tagged labels to enhance ovarian cancer diagnostics.

Biosensors & bioelectronics
We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellu...

Multi-modal predictive modeling of schizophrenia severity: Leveraging liver function indicators and cognitive scores with random forest and SVM.

Psychiatry research. Neuroimaging
Schizophrenia is a complex neuropsychiatric disorder with cognitive deficits and systemic physiological disturbances, including emerging links to hepatic dysfunction via the gut-liver-brain axis. Despite growing evidence, the integration of liver fun...