This paper presents an innovative method for enhance the comprehensibility of Electronic Health Records (EHRs), making it accessible to individuals without specialized clinical knowledge. Our approach entails predicting medical professionals’ impressions, identifying intricate medical terminol- ogy, and clarifying these complex terms. To achieve this, we fine-tuned GPT-3 for predicting doctors’ impressions and integrated the Chain Of Thought (COT) prompting technique to identify and elucidate intricate medical terms. The assessment was conducted using Rouge scores and cosine similarity scores. The outcomes reveal that our proposed approach yields a cosine similarity score surpassing 75, indicative of the model’s exceptional performance. The comparative analysis demonstrates the superiority of our approach concerning doctors’ impressions, detection of complex terminology, and provision of explanations. Furthermore, this work is pioneering in addressing and resolving intricate terminology in EHRs, marking a novel contribution to the field.

This work elucidates the control of integrating non-minimum phase system via series cascade scheme with fractional-order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, which shows the work’s universality. The outer loop controller is suggested in the IMC framework after accountability of fractional-filter and inverse response compensator. This combination is revealed to enhance performance without compromising the robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error (ISE). Two illustrative problems are utilized to examine the recommended control structure’s virtue.

This work elucidates the control of integrating non-minimum phase system via series cascade scheme with fractional-order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, which shows the work’s universality. The outer loop controller is suggested in the IMC framework after accountability of fractional-filter and inverse response compensator. This combination is revealed to enhance performance without compromising the robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error (ISE). Two illustrative problems are utilized to examine the recommended control structure’s virtue.