Work

Machine Learning–Based Prediction of Severe Outcomes in Acute Propranolol Poisoning: An Interpretable Model With Internal Validation

Year: 2026

Type: article

Abstract: Acute propranolol poisoning can cause severe cardiovascular and neurological toxicity, but early risk stratification remains challenging. This is a code base for a machine learning study on a retrospective single-centre cohort of 720 patients. We developed and internally validated machine-learning models for a composite severe in-hospital outcome (death, intensive care unit admission, intubation, vasopressor or inotrope support, coma, seizure or respiratory arrest). At the time of this description, the study is at the peer-review step (not published on the time of this entry).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23913784

Authors: Aboutorabi, Akram, Mostafazadeh, Babak, Rezaei, Kamran, Hosseini, Sayed Masoud, Eslahi, Parnian, rajabi, negin, Shadnia, Shahin, Rahimi, Mitra, erfantalabevini, Peyman

Institutions: Shahid Beheshti University, Shahid Beheshti University of Medical Sciences

Language: English

Topic: Poisoning and overdose treatments

Subfield: Emergency Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7225150519

FWCI:

Cites:

Cited by:

Related to: 10