Social media analysis for e-health and medical purposes

Węgrzyn-Wolska, Katarzyna; Bougueroua, Lamine; Dziczkowski, Grzegorz

doi:10.1109/cason.2011.6085958

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…Social media has become a popular medium for individuals to express opinions and concerns on issues impacting their lives [1][2][3]. In countries without adequate internet infrastructure, like Uganda, communities often use phone-in talk shows on local radio stations for the same purpose.…”

Section: Introductionmentioning

confidence: 99%

ASR-Free CNN-DTW Keyword Spotting Using Multilingual Bottleneck Features for Almost Zero-Resource Languages

Menon

Kamper

Yılmaz

et al. 2018

6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)

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We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting. This forms part of a United Nations effort using keyword spotting to support humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We use 1920 isolated keywords (40 types, 34 minutes) as exemplars for dynamic time warping (DTW) template matching, which is performed on a much larger body of untranscribed speech. These DTW costs are used as targets for a convolutional neural network (CNN) keyword spotter, giving a much faster system than direct DTW. Here we consider how available data from well-resourced languages can improve this CNN-DTW approach. We show that multilingual BNFs trained on ten languages improve the area under the ROC curve of a CNN-DTW system by 10.9% absolute relative to the MFCC baseline. By combining low-resource DTW-based supervision with information from well-resourced languages, CNN-DTW is a competitive option for low-resource keyword spotting.

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Section: Introductionmentioning

confidence: 99%

ASR-Free CNN-DTW Keyword Spotting Using Multilingual Bottleneck Features for Almost Zero-Resource Languages

Menon

Kamper

Yılmaz

et al. 2018

6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)

View full text Add to dashboard Cite

show abstract

“…Operational and performance topics, such as vehicle routing, reverse logistics, purchasing and distribution, skills/competences, and organizational learning, could be identified as well. A trend interaction analysis [89] reveals that the interdependency of logistics with collaboration and information integration plays an important role that may shortly become essential. This may influence strategic perspectives on human resources and knowledge management within logistics (see, for example, [90]).…”

Section: Detection Of Major Trends In Logistics and Trend Interactmentioning

confidence: 99%

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

et al. 2019

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To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a ''double funnel of artificial intelligence.'' Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a ''classical'' literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from ''regular'' review papers; however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed. INDEX TERMS Computational and artificial intelligence, document handling, fuzzy control, knowledge acquisition, pattern analysis, scientific publishing, text mining, text processing.

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“…Academic articles in Medline/PubMed are classified based on text features [26]. For social media analysis of healthcare, text mining and social network analysis are used to propagate infectious diseases with hospital records, predict pandemic increase with Twitter data, model hospital structure network, or analyze health social network for some websites [27]. Some studies applied text mining techniques to analyze the post content in online health forums and groups [28][29][30].…”

Section: B Classification Of User Generated Contentmentioning

confidence: 99%

Classifying User Intention and Social Support Types in Online Healthcare Discussions

Zhang

Yang

2014

2014 IEEE International Conference on Healthcare Informatics

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With the development of online healthcare social media, a large volume of user generated content are available in different health discussion forums or groups. Healthcare social media sites could empower patients to play a substantial role in their treatment by acquiring knowledge and support through actively involving in online discussions and interactions. However, users may have difficulty to find relevant topics or peers in these online health forums with a large amount of unstructured information. Most recommendation systems rely on content-based approach to recommend peers or discussions to their participants.However, in healthcare social media sites, content-based approach is not sufficient because health consumers may have different intentions of participation or may be interest in different types of support even if the content matches their interest. Based on previous studies, we utilize Naïve Bayes methods and propose two tasks for classifying posts and comments on QuitStop forum, an online community for smoking cessation intervention, respectively: (1) classification of intentions and (2) classification of social support types. Different text feature sets and user health feature sets are selected to develop classifiers. Taking different evaluation indicators as optimizing goals, we develop genetic algorithms to combine classifiers with different feature sets and optimize the classification results. It is found that for post classification, integrating text and health features could achieve the highest precision, recall and F1 measure. For comment classification, combining different text features could reach the best result. In the future, the classification result could be applied to developing recommender systems for topic recommendation and user prediction of online health forums.

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Social media analysis for e-health and medical purposes

Cited by 12 publications

References 22 publications

ASR-Free CNN-DTW Keyword Spotting Using Multilingual Bottleneck Features for Almost Zero-Resource Languages

ASR-Free CNN-DTW Keyword Spotting Using Multilingual Bottleneck Features for Almost Zero-Resource Languages

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

Classifying User Intention and Social Support Types in Online Healthcare Discussions

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