Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?

Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors' claims that the proposed model is scalable and investigate whether the tradeoff between prediction specificity (i.e. the ability of the model to predict a wide number of different ailments) and accuracy (i.e. the ability of the model to make the correct prediction) is practically viable. Our experiments conducted on a data corpus of nearly 3,000,000 admissions support the authors' expectations and demonstrate that the high prediction accuracy is maintained well even when the number of admission types explicitly included in the model is increased to account for 98% of all admissions in the corpus. Thus several promising directions for future work are highlighted.Comment: In Proc. International Conference on Bioinformatics and Computational Biology, April 201

Quality-based Multimodal Classification Using Tree-Structured Sparsity

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classification. Among several sparsity models, tree-structured sparsity provides a flexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. To demonstrate their efficacy, the proposed methods are evaluated on three different applications - multiview face recognition, multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014

Entropy Based Template Analysis in Face Biometric Identification Systems

The accuracy of a biometric matching algorithm relies on its ability to better separate score distributions for genuine and impostor subjects. However, capture conditions (e.g. illumination or acquisition devices) as well as factors related to the subject at hand (e.g. pose or occlusions) may even take a generally accurate algorithm to provide incorrect answers. Techniques for face classification are still too sensitive to image distortion, and this limit hinders their use in large-scale commercial applications, which are typically run in uncontrolled settings. This paper will join the notion of quality with the further interesting concept of representativeness of a biometric sample, taking into account the case of more samples per subject. Though being of excellent quality, the gallery samples belonging to a certain subject might be very (too much) similar among them, so that even a moderately different sample of the same subject in input will cause an error. This seems to indicate that quality measures alone are not able to guarantee good performances. In practice, a subject gallery should include a sufficient amount of possible variations, in order to allow correct recognition in different situations. We call this gallery feature representativeness. A significant feature to consider together with quality is the sufficient representativeness of (each) subject’s gallery. A strategy to address this problem is to investigate the role of the entropy, which is computed over a set of samples of a same subject. The paper will present a number of applications of such a measure in handling the galleries of the different users who are registered in a system. The resulting criteria might also guide template updating, to assure gallery representativeness over time

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Tohoku University青木孝文課