Clinical decision-making is complex and has substantial impacts on patients and their families. It involves important uncertainties and trade-offs. Important uncertainties include accuracy of available diagnostic tests, natural history of diseases, effects of a treatment on individual patients, effects of an intervention on populations. Important trade-offs include the immediate risk of a procedure versus improved long-term benefits, such as quality and length of life, a better outcome of a treatment versus a much higher cost associated with that treatment, etc. With complex decision making, it can be difficult to comprehend and compare the benefits and the risks of all available options to make a decision. In such situations, a systematic approach using computational intelligence technologies can facilitate effective clinical decision making.

In the digital era, the widespread use of health information technology makes it easy to collect and store clinical data from a diverse range of sources. These voluminous and complex clinical data combined with the rising computational intelligence technologies, which gives rise to a new area – medical informatics, can be very powerful to provide evidence base to assist clinical decision making.

Medical informatics is an interdisciplinary and scientific field. In this field, researchers develop and apply advanced methods to analyze large amount of health information to better understand the current health care. The goal is to improve health care, via diagnosis, treatment, and prevention of disease, injury, and other physical and mental impairments in human beings. This special issue will focus on medical informatics and computational intelligence technologies for clinical decision support, spanning from theory to practice. Topics on disease prevention, prediction and prognosis are appropriate for this special issue. Practical experiences and experiments in using business intelligence and healthcare information technologies are also welcome. We look forward to contributions from academicians, researchers, and educators worldwide.

Contributions adding to research on computational intelligence technologies for clinical decision support are solicited in, but not limited to, the following topics:

Business Intelligence and Data Warehousing
Cloud Computing and Big Data
Clinical Decision Support System
Health/Medicine informatics education
Feature Selection in Decision Analysis
Risk Evaluation and Modeling
Knowledge Abstraction, Classification & Summarization
Patient Safety and Clinical Outcomes
Public Health Informatics
Statistics and Quality of Medical Data
Survival Analysis and Health Hazard Evaluations
Biomarkers & Clinical Outcomes
Submission instructions
Please note the deadline for this special issue is the 12th December 2018

Editorial information
Principal Guest Editor: Chi-Chang Chang, Chung-Shan Medical University, Taiwan (threec@csmu.edu.tw)
Guest Editor: Tetsuya Sakurai, University of Tsukuba, Japan (sakurai@cs.tsukuba.ac.jp)
Guest Editor: Su-Hsin Chang, Washington University in St. Louis, United States (changsh@wudosis.wustl.edu)
Guest Editor: Chalong Cheewakriangkrai, Chiang Mai University, Thailand (chalong.c@cmu.ac.th)
Guest Editor: Chi-Jie Lu, Chien Hsin University of Science and Technology, Taiwan (jerrylu@uch.edu.tw)