An Enhanced Framework for Pattern Detection in Clinical Laboratory Data.
Year: 2002
Type: article
Abstract: Patient follow-up by Therapeutic Drug Monitoring experts is an effective method of improving drug therapy, but the cost of having such a service track all patient visits is prohibitive. Therefore, we developed a rule-based expert system (LabScanner) that analyzes clinical laboratory drug concentration data from a laboratory information system to identify a subset ofpatients enriched in those who could benefit from expert follow-up.' LabScanner identifies patients based on four rule categories that detect variability, frequency, value and trend patterns, respectively, in clinical laboratory data. This initial framework is able to detect meaningful individual patterns,' but it is limited because it does not allow identification of patients based on multiple patterns with defined temporal relationships.
Source: PubMed Central
Authors: Andrew R. Post, James H. Harrison
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W106106377