In this research, we developed a natural language processing (NLP) framework to investigate the opinions on HPV vaccination reflected on Twitter over a 10-year period-2008-2017. The NLP framework includes sentiment analysis, entity analysis, and artificial intelligence (AI)-based phrase association mining. The sentiment analysis demonstrates the sentiment fluctuation over the past 10 years. The results show that there are more negative tweets in 2008 to 2011 and 2015 to 2016. The entity extraction and analysis help to identify the organization, geographical location and events entities associated with the negative and positive tweets. The results show that the organization entities such as FDA, CDC and Merck occur in both negative and positive tweets of almost every year, whereas the geographical location entities mentioned in both negative and positive tweets change from year to year. The reason is because of the specific events that happened in those different locations. The objective of the AI-based phrase association mining is to identify the main topics reflected in both negative and positive tweets and detailed tweet content. Through the phrase association mining, we found that the main negative topics on Twitter include "injuries", "deaths", "scandal", "safety concerns", and "adverse/ side effects", whereas the main positive topics include "cervical cancers", "cervical screens", "prevents", and "vaccination campaigns". We believe the results of this research can help public health researchers better understand the nature of social media influence on HPV vaccination attitudes and to develop strategies to counter the proliferation of misinformation.
In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets. Of the four methods --Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Document Frequency (DF) and Random Projection (RP) --ICA and LSI are clearly superior when the k-means clustering algorithm is applied, irrespective of the data sets. Random projection consistently returns the worst results, where this appears to be due to the noise distribution characterizing the document clustering task.
There are challenges for analyzing the narrative clinical notes in Electronic Health Records (EHRs) because of their unstructured nature. Mining the associations between the clinical concepts within the clinical notes can support physicians in making decisions, and provide researchers evidence about disease development and treatment. In this paper, in order to model and analyze disease and symptom relationships in the clinical notes, we present a concept association mining framework that is based on word embedding learned through neural networks. The approach is tested using 154,738 clinical notes from 500 patients, which are extracted from the Indiana University Health's Electronic Health Records system. All patients are diagnosed with more than one type of disease. The results show that this concept association mining framework can identify related diseases and symptoms. We also propose a method to visualize a patients' diseases and related symptoms in chronological order. This visualization can provide physicians an overview of the medical history of a patient and support decision making. The presented approach can also be expanded to analyze the associations of other clinical concepts, such as social history, family history, medications, etc.
Mesenchymal stem cells (MSCs) therapy has been applied to a wide range of diseases with excessive immune response, including inflammatory bowel disease (IBD), owing to its powerful immunosuppression and its ability to repair tissue lesions. Different sources of MSCs show different therapeutic properties. Engineering managements are able to enhance the immunomodulation function and the survival of MSCs involved in IBD. The therapeutic mechanism of MSCs in IBD mainly focuses on cell-to-cell contact and paracrine actions. One of the promising therapeutic options for IBD can focus on exosomes of MSCs. MSCs hold promise for the treatment of IBD-associated colorectal cancer because of their tumor-homing function and chronic inflammation inhibition. Encouraging results have been obtained from clinical trials in IBD and potential challenges caused by MSCs therapy are getting solved. This review can assist investigators better to understand the research progress for enhancing the efficacy of MSCs therapy involved in IBD and CAC.
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