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Poisoning

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[Analysis of risk factors for prognosis of patients with acute paraquat intoxication].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To explore the risk factors influencing the prognosis by analyzing clinical data of patients with acute paraquat intoxication, and to assess the prognostic values of acute physiology and chronic health evaluation II (APACHE II) score, sequ...

Comparing answers of artificial intelligence systems and clinical toxicologists to questions about poisoning: Can their answers be distinguished?

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
OBJECTIVE: To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clinical toxicologists and determine whether readers can identify the source of the answers. To evaluate and compare text quality and level of knowledge f...

An efficient machine learning approach for diagnosis of paraquat-poisoned patients.

Computers in biology and medicine
Numerous people die of paraquat (PQ) poisoning because they were not diagnosed and treated promptly at an early stage. Till now, determination of PQ levels in blood or urine is still the only way to confirm the PQ poisoning. In order to develop a new...

Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients.

Computational biology and chemistry
Paraquat (PQ) poisoning seriously harms the health of humanity. An effective diagnostic method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult to identify the patients with low intake of PQ or delayed treatment. Here...

Prediction of acute methanol poisoning prognosis using machine learning techniques.

Toxicology
Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and pro...