AI Medical Compendium Topic:
Databases, Factual

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A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML.

Food chemistry
Taste peptides with umami/bitterness play a role in food attributes. However, the taste mechanisms of peptides are not fully understood, and the identification of these peptides is time-consuming. Here, we created a taste peptide database by collecti...

A machine learning method for predicting the probability of MODS using only non-invasive parameters.

Computer methods and programs in biomedicine
OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform...

A novel deep learning package for electrocardiography research.

Physiological measurement
. In recent years, deep learning has blossomed in the field of electrocardiography (ECG) processing, outperforming traditional signal processing methods in a number of typical tasks; for example, classification, QRS detection and wave delineation. Al...

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (A...

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.

Analytical chemistry
Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were ...

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home.

Sensors (Basel, Switzerland)
Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily...

Pathological Voice Detection Based on Phase Reconstitution and Convolutional Neural Network.

Journal of voice : official journal of the Voice Foundation
The nonlinear dynamic features can effectively describe the acoustic characteristics of normal and pathological voice. In this paper, the phase space reconstruction and convolution neural network are used to classify the normal and pathological voice...

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input ...

Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence: The Future Is Here.

The Journal of bone and joint surgery. American volume
Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinical notes, surgical notes, and medication instructio...

LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis.

Analytical chemistry
Identifying and quantifying cell death is the basis for all cell death research. Current methods for obtaining these quantitative measurements rely on established biomarkers, yet the marker-based approach suffers from limited marker specificity, high...