Two-stage machine learning model for guideline development

We present a Two-Stage Machine Learning (ML) model as a data mining method to develop practice guidelines and apply it to the problem of dementia stag ing. Dementia staging in clinical settings is at present complex and highly subjective because of the ambiguities and the complicated nature of existi ng guidelines. Our model abstracts the two-stage process used by physicians to arrive at the global Clinical Dementia Rating Scale (CDRS) score. The m odel incorporates learning intermediate concepts (CDRS category scores) in the first stage that then become the feature space for the second stage (gl obal CDRS score). The sample consisted of 678 patients evaluated in the Alz heimer's Disease Research Center at the University of California, Irvine. T he demographic variables, functional and cognitive test results used by phy sicians for the task of dementia severity staging were used as input to the machine learning algorithms. Decision tree learners and rule inducers (C4. 5, Cart, C4.5 rules) were selected for our study as they give expressive mo dels, and Naive Bayes was used as a baseline algorithm for comparison purpo ses. We first learned the six CDRS category scores (memory, orientation, ju dgement and problem solving, personal care, home and hobbies, and community affairs). These learned CDRS category scores were then used to learn the g lobal CDRS scores. The Two-Stage ML model classified as well as or better t han the published inter-rater agreements for both the category and global C DRS scoring by dementia experts. Furthermore, for the most critical distinc tion, normal versus very mildly impaired, the Two-Stage ML model was 28.1 a nd 6.6% more accurate than published performances by domain experts. Our st udy of the CDRS examined one of the largest, most diverse samples in the li terature, suggesting that our findings are robust. The Two-Stage ML model a lso identified a CDRS category, Judgment and Problem Solving, which has low classification accuracy similar to published reports. Since this CDRS cate gory appears to be mainly responsible for misclassification of the global C DRS score when it occurs, further attribute and algorithm research on the J udgment and Problem Solving CDRS score could improve its accuracy as well a s that of the global CDRS score. (C) 1999 Elsevier Science B.V. All rights reserved.