The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Semi-supervised metric learning by maximizing constraint margin
Proceedings of the 17th ACM conference on Information and knowledge management
Efficient Euclidean projections in linear time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Two Heads Better Than One: Metric+Active Learning and its Applications for IT Service Classification
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Localized Supervised Metric Learning on Temporal Physiological Data
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Mining diabetes complication and treatment patterns for clinical decision support
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Patient similarity assessment is an important task in the context of patient cohort identif cation for comparative effectiveness studies and clinical decision support applications. The goal is to derive clinically meaningful distance metric to measure the similarity between patients represented by their key clinical indicators. How to incorporate physician feedback with regard to the retrieval results? How to interactively update the underlying similarity measure based on the feedback? Moreover, often different physicians have different understandings of patient similarity based on their patient cohorts. The distance metric learned for each individual physician often leads to a limited view of the true underlying distance metric. How to integrate the individual distance metrics from each physician into a globally consistent unif ed metric? We describe a suite of supervised metric learning approaches that answer the above questions. In particular, we present Locally Supervised Metric Learning (LSML) to learn a generalized Mahalanobis distance that is tailored toward physician feedback. Then we describe the interactive metric learning (iMet) method that can incrementally update an existing metric based on physician feedback in an online fashion. To combine multiple similarity measures from multiple physicians, we present Composite Distance Integration (Comdi) method. In this approach we f rst construct discriminative neighborhoods from each individual metrics, then combine them into a single optimal distance metric. Finally, we present a clinical decision support prototype system powered by the proposed patient similarity methods, and evaluate the proposed methods using real EHR data against several baselines.