Biomedical subjects

M J Pazzani

Publications and source records attributed to M J Pazzani.

4 recordsLinked to original sources

PubMed1999

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 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.

Algorithms↗

PubMed1998

Simple models for estimating dementia severity using machine learning.

Estimating dementia severity using the Clinical Dementia Rating (CDR) Scale is a two-stage process that currently is costly and impractical in community settings, and at best has an interrater reliability of 80%. Because staging of dementia severity is economically and clinically important, we used Machine Learning (ML) algorithms with an Electronic Medical Record (EMR) to identify simpler models for estimating total CDR scores. Compared to a gold standard, which required 34 attributes to derive total CDR scores, ML algorithms identified models with as few as seven attributes. The classification accuracy varied with the algorithm used with naïve Bayes giving the highest. (76%) The mildly demented severity class was the only one with significantly reduced accuracy (59%). If one groups the severity classes into normal, very mild-to-mildly demented, and moderate-to-severely demented, then classification accuracies are clinically acceptable (85%). These simple models can be used in community settings where it is currently not possible to estimate dementia severity due to time and cost constraints.

Algorithms↗

PubMed1998

Guideline generation from data by induction of decision tables using a Bayesian network framework.

Decision tables can be used to represent practice guidelines effectively. In this study we adopt the powerful probabilistic framework of Bayesian Networks (BN) for the induction of decision tables. We discuss the simplest BN model, the Naive Bayes and extend it to the Two-Stage Naive Bayes. We show that reversal of edges in Naive Bayes and Two-stage Naive Bayes results in simple decision table and hierarchical decision table respectively. We induce these graphical models for dementia severity staging using the Clinical Dementia Rating Scale (CDRS) database from the University of California, Irvine, Alzheimer's Disease Research Center. These induced models capture the two-stage methodology clinicians use in computing the global CDR score by first computing the six category scores of memory, orientation, judgment and problem solving, community affairs, home and hobbies and personal care, and then the global CDRS. The induced Two-Stage models also attain a clinically acceptable performance when compared to domain experts and could serve as useful guidelines for dementia severity staging.

Algorithms↗

PubMed1997

Application of an expert system in the management of HIV-infected patients.

A rule-based expert system, Customized Treatment Strategies for HIV (CTSHIV), which encodes information from the literature on known drug-resistant mutations was developed. Additional rules include ranking and weighting based on antiviral activities, redundant mechanisms of action, overlapping toxicities, relative levels of drug-resistance, and proportion of drug-resistant clones in the HIV quasispecies. Plasma was obtained from HIV-infected patients and the RNA was extracted. Segments of the HIV pol gene encoding the entire protease, reverse transcriptase, and integrase proteins were amplified by reverse transcriptase-polymerase chain reaction (using a total of three primer pairs) and cloned. Sequencing was performed on five clones from each of two patients. When the patient's RNA sequencing data were entered into the expert program, and the information was downloaded directly into the CTSHIV program, the five most effective two, three, and four drug regimens coupled with an explanation for their choice were displayed for each patient. Thus, the CTSHIV system couples efficient genetic sequencing with an expert program that recommends regimens based on information in the current medical literature. It may serve as a useful tool in the design of clinical trials and in the management of HIV-infected patients.

Anti-HIV Agents↗