Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models.

Ophthalmic anesthesia the crucial factors in success and safety of ophthalmic surgery, which involves the delicate aspects of pain control, sedation, and patient response. Advances in ophthalmic surgery cause a need for exact and individualized anesthetic procedures to maximize patient satisfaction and outcomes. This research investigates the machine learning (ML) and natural language processing (NLP) to personalize the practice of ophthalmic anesthesia. Text data includes preoperative assessments; drug history, procedure information, and discharge summary are preprocessed using the NLP approach, stop word removal, and lemmatization. Word2Vec technique is applied for feature extraction to represent clinical terms with vectors which carry semantic meaning, helping the model comprehend the text better. This research proposes a ML algorithm of Efficient Osprey Optimized Resilient Random Forest (EOO-RRF) model to forecast ideal anesthesia plans and patient results. Experimental results show that the EOO-RRF model is superior to traditional methods and achieves metrics such as MSE = 28.424, RMSE = 4.321, AUC=98.32% and R2 = 0.956. The results indicate that combining NLP and ML in ophthalmic anesthesia leads to safer, more efficient, and personalized anesthetic management.