An Investigation of Recurrent Neural Architectures for Drug Name Recognition

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand crafted features and domain specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures - the Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.Comment: Accepted for Oral Presentation at LOUHI 2016 : EMNLP 2016 Workshop - The Seventh International Workshop on Health Text Mining and Information Analysis (LOUHI 2016

Bidirectional LSTM-CRF for Clinical Concept Extraction

Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.Comment: This paper "Bidirectional LSTM-CRF for Clinical Concept Extraction" is accepted for short paper presentation at Clinical Natural Language Processing Workshop at COLING 2016 Osaka, Japan. December 11, 201

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Image Anomaly Detection with Capsule Networks and Imbalanced Datasets

Image anomaly detection consists in finding images with anomalous, unusual patterns with respect to a set of normal data. Anomaly detection can be applied to several fields and has numerous practical applications, e.g. in industrial inspection, medical imaging, security enforcement, etc.. However, anomaly detection techniques often still rely on traditional approaches such as one-class Support Vector Machines, while the topic has not been fully developed yet in the context of modern deep learning approaches. In this paper we propose an image anomaly detection system based on capsule networks under the assumption that anomalous data are available for training but their amount is scarce

ON THE ANALYSIS OF THE PERFORMANCES OF PARTICLE SWARM OPTIMIZATION ALGORITHM WITH GLOBALLY AND LOCALLY TUNED INERTIA WEIGHT VARIANTS

The investigation of the performance of Particle Swarm Optimization (PSO) algorithm with the new variants to inertia weight in computing the optimal control of a single stage hybrid system is presented in this paper. Three new variants for inertia weight are defined and their applicability with the PSO algorithm is thoroughly explained. The results obtained through the new proposed methods are compared with the existing PSO algorithm, which has a time varying inertia weight from a higher value to a lower value. The proposed methods provide both faster convergence and optimal solution with better accuracy

Photoacoustic spectroscopy of sprayed CuGaxIn1−xSe2 thin films

CuGaxIn1−xSe2 thin films have been produced using spray-pyrolysis. The layers were deposited onto Mo-coated soda-lime glass at a substrate temperature of 623 K, and the gallium content of the layers varied. The layers were investigated using photoacoustic spectroscopy. The normalised photoacoustic spectra of CuGaxIn1−xSe2 films with x=0, 0.2, 0.4 and 0.5 showed a sudden fall in the photoacoustic signal at photon energies of 0.99, 1.15, 1.27 and 1.36 eV, respectively, which correspond to the respective direct energy band gaps of these films. The photoacoustic spectra also showed peaks at 0.75, 0.78, 0.82, 0.86 and 0.88 eV, respectively, in the sub-band gap region, which have been attributed to the defect states within these films

Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues

We present a learning-based approach to the semantic indexing of multimedia content using cues derived from audio, visual, and text features. We approach the problem by developing a set of statistical models for a predefined lexicon. Novel concepts are then mapped in terms of the concepts in the lexicon. To achieve robust detection of concepts, we exploit features from multiple modalities, namely, audio, video, and text. Concept representations are modeled using Gaussian mixture models (GMM), hidden Markov models (HMM), and support vector machines (SVM). Models such as Bayesian networks and SVMs are used in a late-fusion approach to model concepts that are not explicitly modeled in terms of features. Our experiments indicate promise in the proposed classification and fusion methodologies: our proposed fusion scheme achieves more than 10 % relative improvement over the best unimodal concept detector

Rapid-Learning System for Cancer Care

Compelling public interest is propelling national efforts to advance the evidence base for cancer treatment and control measures and to transform the way in which evidence is aggregated and applied. Substantial investments in health information technology, comparative effectiveness research, health care quality and value, and personalized medicine support these efforts and have resulted in considerable progress to date. An emerging initiative, and one that integrates these converging approaches to improving health care, is “rapid-learning health care.” In this framework, routinely collected real-time clinical data drive the process of scientific discovery, which becomes a natural outgrowth of patient care. To better understand the state of the rapid-learning health care model and its potential implications for oncology, the National Cancer Policy Forum of the Institute of Medicine held a workshop entitled “A Foundation for Evidence-Driven Practice: A Rapid-Learning System for Cancer Care” in October 2009. Participants examined the elements of a rapid-learning system for cancer, including registries and databases, emerging information technology, patient-centered and -driven clinical decision support, patient engagement, culture change, clinical practice guidelines, point-of-care needs in clinical oncology, and federal policy issues and implications. This Special Article reviews the activities of the workshop and sets the stage to move from vision to action