Machine learning-enabled early risk stratification of β-Iactam-induced electrolyte imbalances.

Key Points

• The research aims to develop a model that uses clinical data to predict electrolyte imbalances during beta-lactam treatment.
• Developed a machine learning model utilizing routine clinical variables.
• Conducted external validation to ensure the model's reliability.
• Assessed the performance of the model in identifying risks of electrolyte imbalances.
• Found a high incidence of electrolyte imbalances during beta-lactam therapy.
• Model demonstrated potential for supporting risk-informed monitoring.
• Results indicate the need for improved monitoring practices for at-risk patients.