title: Two-Stage Machine Learning model for guideline development.
abstract: 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 staging. Dementia staging in clinical settings is at present complex and highly subjective because of the ambiguities and the complicated nature of existing guidelines. Our model abstracts the two-stage process used by physicians to arrive at the global Clinical Dementia Rating Scale (CDRS) score. The model incorporates learning intermediate concepts (CDRS category scores) in the first stage that then become the feature space for the second stage (global CDRS score). The sample consisted of 678 patients evaluated in the Alzheimer's Disease Research Center at the University of California, Irvine. The demographic variables, functional and cognitive test results used by physicians 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 models, and Naive Bayes was used as a baseline algorithm for comparison purposes. We first learned the six CDRS category scores (memory, orientation, judgement and problem solving, personal care, home and hobbies, and community affairs). These learned CDRS category scores were then used to learn the global CDRS scores. The Two-Stage ML model classified as well as or better than the published inter-rater agreements for both the category and global CDRS scoring by dementia experts. Furthermore, for the most critical distinction, normal versus very mildly impaired, the Two-Stage ML model was 28.1 and 6.6% more accurate than published performances by domain experts. Our study of the CDRS examined one of the largest, most diverse samples in the literature, suggesting that our findings are robust. The Two-Stage ML model also identified a CDRS category, Judgment and Problem Solving, which has low classification accuracy similar to published reports. Since this CDRS category appears to be mainly responsible for misclassification of the global CDRS score when it occurs, further attribute and algorithm research on the Judgment and Problem Solving CDRS score could improve its accuracy as well as that of the global CDRS score.
authors.affiliation: Department of Information and Computer Science, University of California, Irvine, USA. mani@cbmi.upmc.edu
authors.forename: S
authors.initials: S
authors.lastname: Mani
authors.forename: W R
authors.initials: WR
authors.lastname: Shankle
authors.forename: M B
authors.initials: MB
authors.lastname: Dick
authors.forename: M J
authors.initials: MJ
authors.lastname: Pazzani
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification* / Q000175:diagnosis; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016015:Logistic Models; D017410:Practice Guidelines as Topic