Modelling and assessing one- and two-drug dose titrations
Artificial Intelligence in Medicine(2022)
摘要
In health-care, there is a need to quantify medical errors. Among these errors, we observe wrong dose pre-scriptions. Drug dose titration (DT) is the process by which dosage is progressively adjusted to the patient till a steady dose is reached. Depending on the clinical disease, drug, and patient condition, dose titration can follow different procedures. Once modeled, these procedures can serve for clinical homogenization, standardization, decision support and retrospective analysis. Here, we propose a language to model dose titration procedures. The language was used to formalize one-and two-drug titration of chronic and acute cases, and to perform retro-spective analysis of the drug titration processes on 253 patients diagnosed of diabetes mellitus type 2 and treated with metformin, 321 patients treated of chonic heart failure with furosemide, 155 patients with hyperuricemia treated with allopurinol as initial drug and febuxostat as alternative drug, and 187 hyperuricemia patients with primary drug allopurinol and supplementary drug probenecid, in order to identify different types of drug titration deviations from standard DT methods.
更多查看译文
关键词
Drug dose titration,Medication errors,Knowledge representation,Evidence -based medicine
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要