Assigning labels to clinical text documents is challenging because it requires sophisticated feature engineering, and practically important because of the wide adoption of electronic health record (EHR) systems. In this work, we introduce TextVect - a high throughput, modularized tool that facilitates feature engineering of unstructured clinical text. Empirical evaluation of the various feature representation choices on benchmark clinical text datasets suggests that the term-frequency (tf ) and binary encoding methods are best suited for document level classification. Reducing the number of features through feature selection is also helpful, and the BestFirst method outperforms other popular techniques. The most helpful features are those belonging to controlled vocabularies, the UMLS metathesaurus in particular. For datasets with multiple labels, we empirically evaluate the performance of the state-of-the-art probabilistic classifier chains (PCC) method. Results indicate that the PCC method is a better performer compared to the binary relevance method, where a separate classifier is trained for each label. We discuss these results and demonstrate that TextVect is a valuable tool for feature engineering when applied to classification of unstructured clinical text.

Multilabel learning is an extension of standard binary classification where the goal is to predict a set of labels (we call an individual label a tag) for each input example. The recent probabilistic classifier chain (PCC) method learns a series of probabilistic models that capture tag correlations. In this paper, we show how the PCC model may be viewed as a neural network with connections between output nodes. We then explore the benefits of using a shared hidden layer in the neural network, instead of connections between output nodes. This brings advantages that include tractable test-time inference and removing the need to select a fixed tag ordering.

In this paper, we show how to use the classical technique of beam search for multilabel learning (MLL). A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the computational issue that inference (i.e., predicting the label of an example) requires time exponential in the number of tags. Also, PCC accuracy is sensitive to the ordering of the tags while training. In this work, we show how to use beam search to make inference tractable, and how to integrate beam search with training to determine a suitable tag ordering.

We present TxtVect, a tool for extracting features from clinical documents. It allows for segmentation of documents into paragraphs, sentences, entities, or tokens; and extraction of lexical, syntactic, and semantic features for each of these segments. These features are useful for various machine-learning tasks such as text classification, assertion classification, and relation identification. TxtVect enables users to access these features without installation of the many necessary text processing and NLP tools.

The database of Genotypes and Phenotypes (dbGaP) allows researchers to understand phenotypic contributions in genetic conditions, generate new hypotheses, confirm previous study results, and identify control populations. However, meaningful use of the database is hindered by suboptimal study retrieval. Our objective is to evaluate text classification techniques and feature representation to improve study retrieval in the context of the dbGaP database. This study demonstrated that a combination of n-gram features, metadata features, and χ2 feature selection applied to dbGaP studies increased classification accuracy and F-measure when compared to unigram-based feature representation. We demonstrated that PubMed studies can be used effectively as a surrogate identifier for related dbGaP studies.

In this work, we present a data-driven approach to identifying recreational drug slang. We crawled recreational drug-related Internet forums to build a text corpus and used statistical keyword analysis to identify drug terms. The novelty of this work lies in the application of corpus linguistic techniques to the recreational drug slang domain. We demonstrate the viability of using online sources to mine relevant content and corpus linguistics methodology as a means to discover drug slang terms. In the future, we plan to develop a semi-automated mechanism of discovering drug related slang terms (known and new terms) and structure our taxonomy to include further classes of common slang terms, such as compound drugs and drug paraphernalia.

Grid infrastructures and grid based applications are becoming common approaches for solving large scale science and engineering problems. The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. In this work, we consider an online scheduling problem in immediate mode, where jobs arrive over time and are allocated to machines as soon as they arrive. All jobs’ characteristics are unknown before their arrival times. We implemented several scheduling algorithms and measured three metrics for comparison: response time, bounded slowdown and system utilization. Our simulation allowed us to identify which of the considered methods perform better for response time, bounded slowdown and utilization at different system loads. We also evaluate the usefulness of the methods if certain grid characteristics such as heterogeneity of jobs and resources are known in advance.

We present  a Grid based application which works in collaboration with natural language processing (NLP), to act as a virtual assistant. The application can answer  queries in a conversational  manner  and is  capable of being deployed in various scenarios.  Given the prevalence of large data sources in natural  language engineering and the need for raw  computational  power in analysis of such data, the Grid Computing paradigm provides efficiency and scalability otherwise unavailable  to researchers.  In our  work we explore the integration of  Grid with NLP,  to mine relevant answers  from these distributed resources. Our system receives queries from various interfaces and then uses NLP to understand the domain of the question. The Grid then routes these queries  to the correct knowledge farm, depending on the domain found. Knowledge farms are distributed components which have large annotated domain specific  datasets.  We propose a novel  method which involves  the working of  the Grid and NLP in concert  to mine relevant  information quickly.