Concept formation vs. logistic regression: predicting death in trauma patients.

This study compares two classification models used to predict survival of injured patients entering the emergency department. Concept formation is a machine learning technique that summarizes known examples cases in the form of a tree. After the tree is constructed, it can then be used to predict the classification of new cases.

Extracting knowledge from large medical databases: an automated approach.

Tools which can uncover patterns in patients' records and then make predictions based on that knowledge are and will continue to be high priority in many medical informatics groups. These tools are impacting the performance of outcome studies by discovering patterns which can then be verified with standard statistical tools. This paper demonstrates INC2.

Information, intelligence, and interface: the pillars of a successful medical information system.

This paper addresses three key issues facing developers of clinical and/or research medical information systems. 1. INFORMATION.

Concept formation vs. logistic regression: predicting death in trauma patients.

This paper discusses two classification models, one based on concept formation and the other using standard logistic regression. The models are first explained in some detail and then evaluated on the same population of trauma patients. The goal of both systems is to predict the outcome of those patients.

Turning medical data into decision-support knowledge.

Advances in information collection and analysis are reaching the point of providing physicians with the help of computer-based assistants. These systems will provide rapid second opinions to physicians in a clinical setting as well as assist them in the analysis of large sets of patient descriptions for research purposes. This paper presents INC2.