A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries.

Full Abstract

BACKGROUND: This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries. METHODS: Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process. RESULTS: For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients. CONCLUSION: This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.

Author information

Author/s: Ji, Soo-Yeon (SY); Smith, Rebecca (R); Huynh, Toan (T); Najarian, Kayvan (K);

Affiliation: Department of Computer Science, Virginia Commonwealth University, 401 East Main Street, Richmond, Virginia, USA. jisy(-atsign-)vcu.edu

Journal and publication information

Publication Type: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.

Journal: BMC medical informatics and decision making (BMC Med Inform Decis Mak), published in England. (Language: eng)

Reference: 2009-; vol 9 (issue ) : pp 2

Dates: Created 2009/03/27; Completed 2009/04/14;

PMID: 19144188, status: MEDLINE (last retrieval date: 4/14/2009, IMS Date: 14 Apr 2009 00:00:00)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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