ICSH 2018: LSTM based Sentiment Analysis for Patient Experience Narratives in E-survey Tools

Author(s):  
Chenxi Xia ◽  
Dong Zhao ◽  
Jing Wang ◽  
Jing Liu ◽  
Jingdong Ma
2016 ◽  
Vol 87 (3) ◽  
pp. 377-383 ◽  
Author(s):  
Daniel Noll ◽  
Brendan Mahon ◽  
Bhavna Shroff ◽  
Caroline Carrico ◽  
Steven J. Lindauer

ABSTRACT Objective: To examine the orthodontic patient experience having braces compared with Invisalign by means of a large-scale Twitter sentiment analysis. Materials and Methods: A custom data collection program was created that collected tweets containing the words “braces” or “Invisalign” for a period of 5 months. A hierarchal Naïve Bayes sentiment analysis classifier was developed to sort the tweets into five categories: positive, negative, neutral, advertisement, or not applicable. Each category was then analyzed for specific content. Results: A total of 419,363 tweets applicable to orthodontics were collected. Users posted significantly more positive tweets (61%) than they did negative tweets (39%; P ≤ .0001). There was no significant difference in the distribution of positive and negative sentiment between braces and Invisalign tweets (P = .4189). Positive orthodontics-related tweets often highlighted gratitude for a great smile accompanied with selfies. Negative orthodontic tweets frequently focused on pain. Conclusion: Twitter users expressed more positive than negative sentiment about orthodontic treatment with no significant difference in sentiment between braces and Invisalign tweets.


The Lancet ◽  
2012 ◽  
Vol 380 ◽  
pp. S10 ◽  
Author(s):  
Felix Greaves ◽  
Daniel Ramirez-Cano ◽  
Christopher Millett ◽  
Ara Darzi ◽  
Liam Donaldson

2013 ◽  
Vol 15 (11) ◽  
pp. e239 ◽  
Author(s):  
Felix Greaves ◽  
Daniel Ramirez-Cano ◽  
Christopher Millett ◽  
Ara Darzi ◽  
Liam Donaldson

2017 ◽  
Vol 23 ◽  
pp. 258
Author(s):  
Elizabeth Wendt ◽  
Maria Bates ◽  
Reese Randle ◽  
Jason Orne ◽  
Cameron Macdonald ◽  
...  

2020 ◽  
Author(s):  
LA Evitt ◽  
R Follows ◽  
JH Bentley ◽  
W Williams ◽  
R von Maltzahn

Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.


Author(s):  
Maitane GARCÍA-LÓPEZ ◽  
Ester VAL ◽  
Ion IRIARTE ◽  
Raquel OLARTE

Taking patient experience as a basis, this paper introduces a theoretical framework, to capture insights leading to new technological healthcare solutions. Targeting a recently diagnosed type 1 diabetes child and her mother (the principal caregiver), the framework showed its potential with effective identification of meaningful insights in a generative session. The framework is based on the patient experience across the continuum of care. It identifies insights from the patient perspective: capturing patients´ emotional and cognitive responses, understanding agents involved in patient experience, uncovering pain moments, identifying their root causes, and/or prioritizing actions for improvement. The framework deepens understanding of the patient experience by providing an integrated and multi-leveled structure to assist designers to (a) empathise with the patient and the caregiver throughout the continuum of care, (b) understand the interdependencies around the patient and different agents and (c) reveal insights at the interaction level.


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