Title :
A Hybrid Method to Predict Angina Pectoris through Mining Emergency Data
Author :
Ha, Sung Ho ; Zhang, Zhen Yu ; Kwon, Eun Kyoung
Author_Institution :
Sch. of Bus. Adm., Kyungpook Nat. Univ., Daegu, South Korea
Abstract :
The Emergency Department (ED) has been frustrated by the problems of overcrowding, long waiting times and high costs over decades. With the development of computer techniques, various kinds of information systems have appeared and make people work more effectively, the Emergency Department Information System (EDIS) has been heralded as a "must" for the modern ED. This paper tries to build a hybrid method to predict angina pectoris in the form of EDIS. Based on the frameworks of patients flow in ED, real-world data were collected from the electronic medical records at the ED: more than 210000 records of 842 registered chest pain patients in total. By utilizing the data mining techniques, an expert system was proposed to help physicians with faster and more accurate decision making of diagnosis and lab test selections when they are diagnosing with angina pectoris patients.
Keywords :
cardiology; data mining; diseases; medical expert systems; medical information systems; patient diagnosis; angina pectoris; chest pain patient; data mining; electronic medical records; emergency department information system; expert system; hybrid method; patient diagnosis; physicians; Biomedical imaging; Costs; Data mining; Decision making; Diagnostic expert systems; Hospitals; Information systems; Medical diagnostic imaging; Pain; Predictive models;
Conference_Titel :
Information Science and Applications (ICISA), 2010 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-5941-4
Electronic_ISBN :
978-1-4244-5943-8
DOI :
10.1109/ICISA.2010.5480410
