Detail publikace

A Novel Clinical Expert System for Chest Pain Risk Assessment

ATASSI, H. FAROOQ, K. HUSSAIN, A.

Originální název

A Novel Clinical Expert System for Chest Pain Risk Assessment

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

Rapid access chest pain clinics (RACPC) enable clinical risk assessment, investigation and arrangement of a treatment plan for chest pain patients without a long waiting list. RACPC Clinicians often experience difficulties in the diagnosis of chest pain due to the inherent complexity of the clinical process and lack of comprehensive automated diagnostic tools. To date, various risk assessment models have been proposed, inspired by the National Institute of Clinical Excellence (NICE) guidelines to provide clinical decision support mechanism in chest pain diagnosis. The aim of this study is to help improve the performance of RACPC, specifically from the clinical decision support perspective. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating 14 risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. In the first phase, a novel binary classification model was developed using a Decision Tree algorithm in conjunction with forward and backward selection wrapping techniques. Secondly, a logistic regression model was trained using all of the given variables combined with forward and backward feature selection techniques to identify the most significant features. The new models have resulted in very good predictive power, demonstrating general performance improvement compared to a state-of-the-art prediction model.

Klíčová slova

RACPC risk assessment, Chest pain decision support system, Clinical decision support system for chest pain based on NICE Guidelines.

Autoři

ATASSI, H.; FAROOQ, K.; HUSSAIN, A.

Rok RIV

2013

Vydáno

9. 6. 2013

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

2013

Číslo

7888

Stát

Spolková republika Německo

Strany od

296

Strany do

307

Strany počet

12

BibTex

@article{BUT103101,
  author="Hicham {Atassi} and Kamran {Farooq} and Amir {Hussain}",
  title="A Novel Clinical Expert System for Chest Pain Risk Assessment",
  journal="Lecture Notes in Computer Science",
  year="2013",
  volume="2013",
  number="7888",
  pages="296--307",
  issn="0302-9743"
}