Determinants of Intravenous Infusion Longevity and Infusion Failure via a Nonlinear Model Analysis of Smart Pump Event Logs: Retrospective Study.
Arash KiaJames WatersonNorma BargaryStuart RoltKevin BurkeJeremy RobertsonSamuel GarciaAlessio BenavoliDavid BergströmPublished in: JMIR AI (2023)
This study provides clinicians with insights into the specific types of infusion that warrant more intense observation or proactive management of intravenous access; additionally, it can offer valuable information regarding the average duration of uninterrupted infusions that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions, achieved through compounding to create customized concentrations for individual patients, may be possible in light of the study's outcomes. The study also highlights the potential of machine learning nonlinear models in predicting outcomes and life spans of specific therapies delivered via medical devices.
Keyphrases
- machine learning
- low dose
- palliative care
- end stage renal disease
- healthcare
- type diabetes
- newly diagnosed
- chronic kidney disease
- metabolic syndrome
- skeletal muscle
- prognostic factors
- peritoneal dialysis
- adipose tissue
- ejection fraction
- chronic pain
- artificial intelligence
- glycemic control
- human health
- direct oral anticoagulants