Douglas P Hardin

Text Categorization Models for High-Quality Article Retrieval in Internal Medicine

OBJECTIVE Finding the best scientific evidence that applies to a patient problem is becoming exceedingly difficult due to the exponential growth of medical publications. The objective of this study was to apply machine learning techniques to automatically identify high-quality, content-specific articles for one time period in internal medicine and compare their performance with previous Boolean-based PubMed clinical query filters of Haynes et al. DESIGN The selection criteria of the ACP Journal Club for articles in internal medicine were the basis for identifying high-quality articles in the areas of etiology, prognosis, diagnosis, and treatment. Naive Bayes, a specialized AdaBoost algorithm, and linear and polynomial support vector machines were applied to identify these articles. MEASUREMENTS The machine learning models were compared in each category with each other and with the clinical query filters using area under the receiver operating characteristic curves, 11-point average recall precision, and a sensitivity/specificity match method. RESULTS In most categories, the data-induced models have better or comparable sensitivity, specificity, and precision than the clinical query filters. The polynomial support vector machine models perform the best among all learning methods in ranking the articles as evaluated by area under the receiver operating curve and 11-point average recall precision. CONCLUSION This research shows that, using machine learning methods, it is possible to automatically build models for retrieving high-quality, content-specific articles using inclusion or citation by the ACP Journal Club as a gold standard in a given time period in internal medicine that perform better than the 1994 PubMed clinical query filters.

A theoretical characterization of linear SVM-based feature selection

Most prevalent techniques in Support Vector Machine (SVM) feature selection are based on the intuition that the weights of features that are close to zero are not required for optimal classification. In this paper we show that indeed, in the sample limit, the irrelevant variables (in a theoretical and optimal sense) will be given zero weight by a linear SVM, both in the soft and the hard margin case. However, SVM-based methods have certain theoretical disadvantages too. We present examples where the linear SVM may assign zero weights to strongly relevant variables (i.e., variables required for optimal estimation of the distribution of the target variable) and where weakly relevant features (i.e., features that are superfluous for optimal feature selection given other features) may get non-zero weights. We contrast and theoretically compare with Markov-Blanket based feature selection algorithms that do not have such disadvantages in a broad class of distributions and could also be used for causal discovery.

Low Complexity Methods For Discretizing Manifolds Via Riesz Energy Minimization

Let

Polarization Optimality of Equally Spaced Points on the Circle for Discrete Potentials

A

Fractal function, splines, intertwining multiresolution analysis, and wavelets

A be a compact

Large Deviation Principles for Hypersingular Riesz Gases

d