687 research outputs found
Strategy training in the classroom to improve listening skills
Mexican students learning English as a second language (ESL) face difficulties in listening due to factors such as anxiety and lack of strategies to deal with listening. Some listening training sessions to provide learners with memory, cognitive, and compensation strategies like identifying key words, getting the main idea, making predictions, inferences that help them to comprehend the speaker’s message and to manage this problem took place and results are reported here. The aim is to investigate whether providing explicit strategy training helps to improve listening skills, and how much influence the training has in the Intervention Group (IG) in comparison to two control groups (CGs). To that end, the IG and the two CGs were tested before and after receiving training and the scores were computed using a correlated samples t-test (t) as well as ANOVA (F) - statistics of group differences. The ANOVA (F) outcomes indicated non- statistically significant differences in the three groups for listening skills. Correlated samples t-tests (t) findings showed non significant results for two groups (intervention group (IG) and control group one (CG1)) while for control group two (CG2) was the opposite. The results suggest that explicit strategy training is not the only factor that affects listening improvement. A positive increase in the use of memory, cognitive, and compensation strategies was found
Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts
Social Network Analysis (SNA) of organizations can attract great interest
from government agencies and scientists for its ability to boost translational
research and accelerate the process of converting research to care. For SNA of
a particular disease area, we need to identify the key research groups in that
area by mining the affiliation information from PubMed. This not only involves
recognizing the organization names in the affiliation string, but also
resolving ambiguities to identify the article with a unique organization. We
present here a process of normalization that involves clustering based on local
sequence alignment metrics and local learning based on finding connected
components. We demonstrate the application of the method by analyzing
organizations involved in angiogenensis treatment, and demonstrating the
utility of the results for researchers in the pharmaceutical and biotechnology
industries or national funding agencies.Comment: This paper has been withdrawn; First International Workshop on Graph
Techniques for Biomedical Networks in Conjunction with IEEE International
Conference on Bioinformatics and Biomedicine, Washington D.C., USA, Nov. 1-4,
2009; http://www.public.asu.edu/~sjonnal3/home/papers/IEEE%20BIBM%202009.pd
Social media mining for identification and exploration of health-related information from pregnant women
Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper, we explore the possibility of utilizing social media to perform
targeted data collection and analysis from a particular population group --
pregnant women. We hypothesize that we can use social media to identify cohorts
of pregnant women and follow them over time to analyze crucial health-related
information. To identify potentially pregnant women, we employ simple
rule-based searches that attempt to detect pregnancy announcements with
moderate precision. To further filter out false positives and noise, we employ
a supervised classifier using a small number of hand-annotated data. We then
collect their posts over time to create longitudinal health timelines and
attempt to divide the timelines into different pregnancy trimesters. Finally,
we assess the usefulness of the timelines by performing a preliminary analysis
to estimate drug intake patterns of our cohort at different trimesters. Our
rule-based cohort identification technique collected 53,820 users over thirty
months from Twitter. Our pregnancy announcement classification technique
achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
timelines. Analysis of the timelines revealed that pertinent health-related
information, such as drug-intake and adverse reactions can be mined from the
data. Our approach to using user timelines in this fashion has produced very
encouraging results and can be employed for other important tasks where
cohorts, for which health-related information may not be available from other
sources, are required to be followed over time to derive population-based
estimates.Comment: 9 page
Enhancing clinical concept extraction with distributional semantics
AbstractExtracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text.The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task.The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged F-score for exact match increased from 80.3% to 82.3% and the micro-averaged F-score based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data
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