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Since the number of CT slices can be quite large, it is important to develop a computer aided decision making system to analyze the data, because in general only a small portion of the dataset becomes important in establishing a diagnostic [ 6].
The case study demonstrates capabilities as well as boundaries of computer-aided decision making under the premise of incomplete information.
This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms.
Our intent is to extract and formulate medical diagnostic knowledge into an appropriate set of transparent decision rules that can be used in a computer-assisted decision making system.
Software that provides the computer-aided decision making system will be optimized and made available to the academic community as a web-based application, as well as a software tool on portable personal computing devices.
We hypothesize that a rule-based system, attractive to physicians as the reasoning behind the rules is transparent and easy to understand, can be as accurate as "black-box" methods such as neural networks and SVM. 2. We hypothesize that when trained correctly, a computer-aided decision making system can provide clinically useful rules with a high degree of accuracy.
Although several computer-assisted trauma decision making systems already exist, the majority of the systems rely solely on patient demographics to find similar cases in trauma databases and provide a recommendation based on these cases.
There has been recent interest on the impact of emotional expressions of computers on people's decision making.
Progress in PSE is closely related to computer-based tools for improved decision making.
Computer-aided systems can impact trauma decision making by increasing decision accuracy and reducing time for decision making.
Experts had little doubt about the importance of computer decision-making support for the most frequent and demanding illnesses, prescriptions, therapy control, and health care prevention.
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