Using text and visual mining to analyze clinical diagnosis records

Title:

Using text and visual mining to analyze clinical diagnosis records

Creator:

Chen, Chien-Hsing and Hsu, Chung-Chian

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:279c012d-5232-47d2-a949-ed5e30a27a2f
uuid:279c012d-5232-47d2-a949-ed5e30a27a2f
doi:10.14311/NNW.2012.22.028

Subject:

Clinical diagnosis record, ICD-9 code, keyword extraction, ViSOM, and SOM

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Hospitals must index each case of inpatient medical care with codes from the International Classification of Diseases, 9th Revision (ICD-9), under regulations from the Bureau of National Health Insurance. This paper aims to investigate the analysis of free-textual clinical medical diagnosis documents with ICD-9 codes using state-of-the-art techniques from text and visual mining fields. In this paper, ViSOM and SOM approaches inspire several analyses of clinical diagnosis records with ICD-9 codes. ViSOM and SOM are also used to obtain interesting patterns that have not been discovered with traditional, nonvisual approaches. Furthermore, we addressed three principles that can be used to help clinical doctors analyze diagnosis records effectively using the ViSOM and SOM approaches. The experiments were conducted using real diagnosis records and show that ViSOM and SOM are helpful for organizational decision-making activities.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:5

Harvested from:

CDK

Metadata only:

false