Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models

Background: In this paper we present the approaches and methods employed in order to deal with a large scale multi-label semantic indexing task of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge of 2014. Methods: The main contribution of this work is a multi-label ensemble method that incorporates a McNemar statistical significance test in order to validate the combination of the constituent machine learning algorithms. Some secondary contributions include a study on the temporal aspects of the BioASQ corpus (observations apply also to the BioASQ's super-set, the PubMed articles collection) and the proper adaptation of the algorithms used to deal with this challenging classification task. Results: The ensemble method we developed is compared to other approaches in experimental scenarios with subsets of the BioASQ corpus giving positive results. During the BioASQ 2014 challenge we obtained the first place during the first batch and the third in the two following batches. Our success in the BioASQ challenge proved that a fully automated machine-learning approach, which does not implement any heuristics and rule-based approaches, can be highly competitive and outperform other approaches in similar challenging contexts

Deep Neural Networks for Multi-Label Text Classification: Application to Coding Electronic Medical Records

Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders. The main difficulty with developing automated EMR coding methods is the nature of the label space. The standardized vocabularies used for medical coding contain over 10 thousand codes. The label space is large, and the label distribution is extremely unbalanced - most codes occur very infrequently, with a few codes occurring several orders of magnitude more than others. A few codes never occur in training dataset at all. In this work, we present three methods to handle the large unbalanced label space. First, we study how to augment EMR training data with biomedical data (research articles indexed on PubMed) to improve the performance of standard neural networks for text classification. PubMed indexes more than 23 million citations. Many of the indexed articles contain relevant information about diagnosis and procedure codes. Therefore, we present a novel method of incorporating this unstructured data in PubMed using transfer learning. Second, we combine ideas from metric learning with recent advances in neural networks to form a novel neural architecture that better handles infrequent codes. And third, we present new methods to predict codes that have never appeared in the training dataset. Overall, our contributions constitute advances in neural multi-label text classification with potential consequences for improving EMR coding

Extended Edgecluster based Technique for Social Networking Collective Behavior Learning System

Growing interest and continuous development of social network sites like Facebook Twitter Flicker and YouTube etc turn to several researchers for research study planning and rigorous development Exact people behavior prediction is the most important challenge of these on-line social networking websites This research focus to learn to predict collective behavior in social media networks Particularly provided information about some person how can we collect the behavior of unobserved persons in the same network These tremendous growing networks in social media are of massive size involving large number of actors The computational scale of these networks makes necessary scalable learning for models for collective collaborative behavior prediction This scalability issue is solved by the proposed k-means clustering algorithm which is used to partition the edges into disjoint distinct sets with each set is showing one separate affiliation This edge-centric structure represents that the extracted social dimensions are definitely sparse in nature This model idealized on the sparse natured social dimensions shows efficient prediction performance than earlier existing approaches The proposed approach can effectively able to work for sparse social networks of any growing size The important advantage of this method is that it easily grows upon to handle networks with large number of actors while existing methods was unable to do This scalable approach effectively used over of online network collective behavior on a large scal

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201