A semantically enriched text mining system for clinical decision support

2020 ◽  
Author(s):  
Carmen Luque ◽  
José M. Luna ◽  
Sebastián Ventura
Keyword(s):  
Decision Support ◽  
Text Mining ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Mining System ◽  
Text Mining System

Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

2017 ◽  
pp. 184-201 ◽  
Author(s):  
Jitendra Jonnagaddala ◽  
Hong-Jie Dai ◽  
Pradeep Ray ◽  
Siaw-Teng Liaw
Keyword(s):  
Decision Support ◽  
Text Mining ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Association Studies ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Genome Wide ◽  
Electronic Health

Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.

Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

2016 ◽  
pp. 252-269 ◽  
Author(s):  
Jitendra Jonnagaddala ◽  
Hong-Jie Dai ◽  
Pradeep Ray ◽  
Siaw-Teng Liaw
Keyword(s):  
Decision Support ◽  
Text Mining ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Association Studies ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Genome Wide ◽  
Electronic Health

Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision

Clinical Decision Support: An Overview

2012 ◽  
Author(s):  
Blackford Middleton
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

Sign in / Sign up

Export Citation Format

Share Document