IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making
View Sample PDF
Author(s): William Claster (Ritsumeikan Asia Pacific University, Japan), Nader Ghotbi (Ritsumeikan Asia Pacific University, Japan)and Subana Shanmuganathan (Auckland University of Technology, New Zealand)
Copyright: 2010
Pages: 13
Source title: Biomedical Knowledge Management: Infrastructures and Processes for E-Health Systems
Source Author(s)/Editor(s): Wayne Pease (University of Southern Queensland, Australia), Malcolm Cooper (Ritsumeikan Asia Pacific University, Japan)and Raj Gururajan (University of Southern Queensland, Australia)
DOI: 10.4018/978-1-60566-266-4.ch002

Purchase

View The Use of Artificial Intelligence Systems for Support of Medical Decision-Making on the publisher's website for pricing and purchasing information.

Abstract

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

Related Content

Saloua Mabsor-Zgandaoui, Khawla Rachmoune, Ilham Aftais, Fatima Ezzahra Elamrani, Imade Amradi, Adil El Housseini, Youssef Ait Hamdan, Youness Zgandaoui, Abdelghani Iddar, Mohammed El Mzibri, Adnane Moutaouakkil, Aboubaker El Hessni, Abdelhalim Mesfioui. © 2026. 30 pages.
Yusuf Olatunji Waidi. © 2026. 20 pages.
Ajinkya Nene, Sorour Sadeghzade, Wenjie Yang, Prakash Somani. © 2026. 12 pages.
Seyyed Mohammad Amin Mousavi-Sagharchi, Mahdieh Ranjbar-Jamalabadi, Sama Yavari, Elina Afrazeh, Naresh Poondla, Mohsen Sheykhhasan. © 2026. 32 pages.
Wenqiang Xie, Yuan Su, Ruiqi Zhang, Sijia Li, Jia Ni, Longquan Shao. © 2026. 18 pages.
Zhengao Wang, Huiyu Zhao, Yao Han, Wuyi Zhou, Chengyun Ning. © 2026. 30 pages.
Navya Aggarwal, Shinjini Sen, Tanmay J. Urs, Shreya Gupta, Banashree Bondhopadhyay. © 2026. 36 pages.
Body Bottom