Fuzzy Interface Drug Delivery Decision‐Making Algorithm
Yogendra Narayan, Mukta Sandhu, Yousef A. Baker El–Ebiary, N. Ashokkumar · 2025
Maintaining a reliable medication supply that meets patients’ needs is one of medicine's most significant problems. To provide fully automated solutions based on feedback variables, closed-loop techniques have seen extensive application. However, this becomes a challenging problem to address when the variable of interest is not easily measurable, or when there is a lack of background information supporting the process. In this study, a brand-new method for solving this issue is introduced. The primary goal of this research is to define an automated system that can lead the medication supply decision-making process while also considering the effect of a specific clinical characteristic. This novel method will thus verify a particular physiological signal for use as a feedback variable in the titration of drugs. An FDS for the medication distribution method will be defined as a byproduct of the algorithm's output in fuzzy rules and membership functions. The decision tree approach, the suggested method, derives its structure from a Fuzzy Inference System (FIS). Data group, preprocessing, FIS development, and result validation are the four steps established in this technique. The Analgesia Nociception Index has been investigated by way of a potential criterion for directing analgesia during surgical operations. Fifteen actual patients who had cholecystectomy procedures provided the data used in this study.