Automatic classification and storage method of electronic medical records based on improved genetic algorithm

This paper gives an automated category and storage method for electronic clinical records utilizing an stepped forward genetic set of rules (IGA).Effective management of digital medical facts is fundamental to medical informatization.But, the sheer extent and complexity of statistics render traditional guide classification and storage methods insufficient.To deal with this assignment, the paper first opinions present EMR type and storage techniques, encompassing rule-based and facts-primarily based techniques, and evaluates their deserves and boundaries.In the end, it introduces the genetic set of rules as an optimization technique, illustrating its various applications throughout various domains.Building upon this foundation, the paper proposes an IGA tailored to the unique characteristics of EMRs.The improvements encompass person coding, fitness characteristic format, and optimization of genetic operations.Via empirical validation, the proposed method demonstrates superior overall performance in EMR category and garage obligations, boasting stronger accuracy and efficiency as compared to existing methodologies.This studies offers a novel answer for EMR control, thereby advancing the progress of medical informatization.