Deep learning neural network derivation and testing to distinguish acute poisonings
O Mehrpour, C Hoyte, A Al Masud… - Expert Opinion on …, 2023 - Taylor & Francis
Introduction Acute poisoning is a significant global health burden, and the causative agent is
often unclear. The primary aim of this pilot study was to develop a deep learning algorithm …
often unclear. The primary aim of this pilot study was to develop a deep learning algorithm …
The role and promise of artificial intelligence in medical toxicology
Artificial intelligence (AI) refers to machines or software that process information and interact
with the world as understanding beings. Examples of AI in medicine include the automated …
with the world as understanding beings. Examples of AI in medicine include the automated …
Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the …
Background Biguanides and sulfonylurea are two classes of anti-diabetic medications that
have commonly been prescribed all around the world. Diagnosis of biguanide and …
have commonly been prescribed all around the world. Diagnosis of biguanide and …
Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System …
Clinical effects of antihyperglycemic agents poisoning may overlap each other. So,
distinguishing exposure to these pharmaceutical drugs may take work. This study examined …
distinguishing exposure to these pharmaceutical drugs may take work. This study examined …