Automatic disease coding method and system

Abstract

The invention discloses an automatic disease coding method and system. The method comprises the following steps: input data is received; the input data comprises original diagnosis data and medical history file data, the input original diagnosis data and medical history file data are subjected to preprocessing operation, whether a result can be obtained is determined after searching operation is conducted in standard classification and coding of ICD-10; if no, the original diagnosis data is subjected to word segmenting operation, associating and converting operation and matching tree searchingoperation; an optimal result is screened out from matching tree processing results; if the result is not optimal, the result is split; each single disease name is subjected to the word segmenting operation, the associating and converting operation and the matching tree searching operation; the optimal result is screened out from matching tree processing results or the medical history file data, coding accuracy of the result is assessed, and a coding result and an accuracy assessment result are output. A problem that standard diagnosis coding systems adopted by all medical institutions are notunified can be effectively solved.

Description

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AI Technical Summary

Benefits of technology

The technical effect that this patented technology helps improve upon existing methods for searching through healthcare data by providing an understanding about how different types or forms are related based on their respective meanings rather than just stating what they're supposedly doing well with them. This makes researchers more efficient ways to find relevant knowledge from these sources without compromising any trustworthiness standards associated therewith.

Problems solved by technology

Technological Problem addressed in this patents relates to ensuring accurate documentation of patient identification and evaluation during medicine practice. Current methods involve collecting and analyzing large amounts of technical documents such as electronic healthcare systems and government regulations, but they lack precision because of variations among practitioner preferences over multiple generators who may differ greatly depending upon individual factors like race/age, ethnic origins, education levels, educational institution qualifications, and cultural perspectives. Additionally, existing methodologies require significant effort and resources, making them difficult to implement across borders without consolidating into one central repository system.

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