Extracting Conceptual Terms from Medical Documents

Automated biomedical concept recognition is important for biomedical document retrieval and text mining research. In this paper, we describe a two-step concept extraction technique for documents in biomedical domain. Step one includes noun phrase extraction, which can automatically extract noun phrases from medical documents. Extracted noun phrases are used as concept term candidates which become inputs of next step. Step two includes keyphrase extraction, which can automatically identify important topical terms from candidate terms. Experiments were conducted to evaluate results of both steps. The experiment results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying document conceptual terms

Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery

Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc

Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context

Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus. Several topic models have already exploited word embeddings to recognize local context, however, it has been weakly combined with the global context during the topic inference. This paper proposes to extract topical phrases corroborating the word embedding information with the global context detected by Latent Semantic Analysis, and then combine them by means of the Polya urn ´ model. To highlight the effectiveness of this combined approach the model was assessed analyzing clinical reports, a challenging scenario characterized by technical jargon and a limited word statistics available. Results show it outperforms the state-of-the-art approaches in terms of both topic coherence and computational cost

Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications

With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications.Comment: 15 pages, 9 figure

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd

Hierarchical viewpoint discovery from tweets using Bayesian modelling

When users express their stances towards a topic in social media, they might elaborate their viewpoints or reasoning. Oftentimes, viewpoints expressed by different users exhibit a hierarchical structure. Therefore, detecting this kind of hierarchical viewpoints offers a better insight to understand the public opinion. In this paper, we propose a novel Bayesian model for hierarchical viewpoint discovery from tweets. Driven by the motivation that a viewpoint expressed in a tweet can be regarded as a path from the root to a leaf of a hierarchical viewpoint tree, the assignment of the relevant viewpoint topics is assumed to follow two nested Chinese restaurant processes. Moreover, opinions in text are often expressed in un-semantically decomposable multi-terms or phrases, such as ‘economic recession’. Hence, a hierarchical Pitman–Yor process is employed as a prior for modelling the generation of phrases with arbitrary length. Experimental results on two Twitter corpora demonstrate the effectiveness of the proposed Bayesian model for hierarchical viewpoint discovery