A Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learning.

Diabetes can be diagnosed by either Fasting Plasma Glucose or Hemoglobin A1c. The aim of our study was to explore the differences between the two criteria through the development of a machine learning based diabetes diagnostic algorithm and analysing the predictive contribution of each input biomarker. Our study concludes that fasting insulin is predictive of diabetes defined by FPG, but not by HbA1c. Besides, 28 other fasting blood biomarkers were not significant predictors of diabetes.

Comparison of Word and Character Level Information for Medical Term Identification Using Convolutional Neural Networks and Transformers.

Complexity and domain-specificity make medical text hard to understand for patients and their next of kin. To simplify such text, this paper explored how word and character level information can be leveraged to identify medical terms when training data is limited. We created a dataset of medical and general terms using the Human Disease Ontology from BioPortal and Wikipedia pages. Our results from 10-fold cross validation indicated that convolutional neural networks (CNNs) and transformers perform competitively. The best F score of 93.9% was achieved by a CNN trained on both word and character level embeddings. Statistical significance tests demonstrated that general word embeddings provide rich word representations for medical term identification. Consequently, focusing on words is favorable for medical term identification if using deep learning architectures.

What Does Eye-Blink Rate Variability Dynamics Tell Us About Cognitive Performance?

Cognitive performance is defined as the ability to utilize knowledge, attention, memory, and working memory. In this study, we briefly discuss various markers that have been proposed to predict cognitive performance. Next, we develop a novel approach to characterize cognitive performance by analyzing eye-blink rate variability dynamics. Our findings are based on a sample of 24 subjects. The subjects were given a 5-min resting period prior to a 10-min IQ test. During both stages, eye blinks were recorded from Fp1 and Fp2 electrodes. We found that scale exponents estimated for blink rate variability during rest were correlated with subjects' performance on the subsequent IQ test. This surprising phenomenon could be explained by the person to person variation in concentrations of dopamine in PFC and accumulation of GABA in the visual cortex, as both neurotransmitters play a key role in cognitive processes and affect blinking. This study demonstrates the possibility that blink rate variability dynamics at rest carry information about cognitive performance and can be employed in the assessment of cognitive abilities without taking a test.