Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning.

BACKGROUND: Though shock wave lithotripsy (SWL) has developed to be one of the most common treatment approaches for nephrolithiasis in recent decades, its treatment planning is often a trial-and-error process based on physicians' subjective judgement. Physicians' inexperience with this modality can lead to low-quality treatment and unnecessary risks to patients. OBJECTIVE: To improve the quality and consistency of shock wave lithotripsy treatment, we aimed to develop a deep learning model for generating the next treatment step by previous steps and preoperative patient characteristics and to produce personalized SWL treatment plans in a step-by-step protocol based on the deep learning model. METHODS: We developed a deep learning model to generate the optimal power level, shock rate, and number of shocks in the next step, given previous treatment steps encoded by long short-term memory neural networks and preoperative patient characteristics. We constructed a next-step data set (N=8583) from top practices of renal SWL treatments recorded in the International Stone Registry. Then, we trained the deep learning model and baseline models (linear regression, logistic regression, random forest, and support vector machine) with 90% of the samples and validated them with the remaining samples. RESULTS: The deep learning models for generating the next treatment steps outperformed the baseline models (accuracy = 98.8%, F1 = 98.0% for power levels; accuracy = 98.1%, F1 = 96.0% for shock rates; root mean squared error = 207, mean absolute error = 121 for numbers of shocks). The hypothesis testing showed no significant difference between steps generated by our model and the top practices (P=.480 for power levels; P=.782 for shock rates; P=.727 for numbers of shocks). CONCLUSIONS: The high performance of our deep learning approach shows its treatment planning capability on par with top physicians. To the best of our knowledge, our framework is the first effort to implement automated planning of SWL treatment via deep learning. It is a promising technique in assisting treatment planning and physician training at low cost.

Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits.

BACKGROUND: Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. OBJECTIVE: This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. METHODS: We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency-inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. RESULTS: Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being <21 years old. (3) The most common key phrases used in selling threads were "WTS," "want to sell," "for sale," and "selling" (154/214, 72.0%). The most common key phrases used in buying threads were "look for/get JUUL/pods" (58/168, 34.5%) and "WTB" (53/168, 31.5%). (4) The most important concern that UnderageJuul Redditors had in obtaining JUULs was the price (311/1306, 23.81%), followed by the delivery service (68/1306, 5.21%). (5) The most popular flavors among the users with buying/selling intention were mango, cucumber, and mint. The flavor preferences remained consistent on both subreddits. Adverse symptoms related to the mango flavor were reported by 3 users on the JUUL subreddit. (6) In total, 24.4% (49/201) of users wanted to buy JUULs and 46.6% (83/178) of users wanted to sell JUULs, including 11 self-reported underage users, who also participated in the discussions on the JUUL subreddit. (7) Within one year of the UnderageJuul subreddit shutting down, there were 40 users who continued to post 186 threads on the JUUL subreddit, including 10 threads indicating buying/selling willingness that were posted shortly after the UnderageJuul subreddit was closed. CONCLUSIONS: There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials.

Public Reactions to the Cigarette Control Regulation on a Chinese Microblogging Platform: Empirical Analysis.

BACKGROUND: On January 1, 2019, a new regulation on the control of smoking in public places was officially implemented in Hangzhou, China. On the day of the implementation, a large number of Chinese media reported the contents of the regulation on the microblog platform Weibo, causing a strong response from and heated discussion among netizens. OBJECTIVE: This study aimed to conduct a content and network analysis to examine topics and patterns in the social media response to the new regulation. METHODS: We analyzed all microblogs on Weibo that mentioned and explained the regulation in the first 8 days following the implementation. We conducted a content analysis on these microblogs and used social network visualization and descriptive statistics to identify key users and key microblogs. RESULTS: Of 7924 microblogs, 12.85% (1018/7924) were in support of the smoking control regulation, 84.12% (6666/7924) were neutral, and 1.31% (104/7924) were opposed to the smoking regulation control. For the negative posts, the public had doubts about the intentions of the policy, its implementation, and the regulations on electronic cigarettes. In addition, 1.72% (136/7924) were irrelevant to the smoking regulation control. Among the 1043 users who explicitly expressed their positive or negative attitude toward the policy, a large proportion of users showed supportive attitudes (956/1043, 91.66%). A total of 5 topics and 11 subtopics were identified. CONCLUSIONS: This study used a content and network analysis to examine topics and patterns in the social media response to the new smoking regulation. We found that the number of posts with a positive attitude toward the regulation was considerably higher than that of the posts with a negative attitude toward the regulation. Our findings may assist public health policy makers to better understand the policy's intentions, scope, and potential effects on public interest and support evidence-based public health regulations in the future.

Underage JUUL Use Patterns: Content Analysis of Reddit Messages.

BACKGROUND: The popularity of JUUL (an e-cigarette brand) among youth has recently been reported in news media and academic papers, which has raised great public health concerns. Little research has been conducted on the age distribution, geographic distribution, approaches to buying JUUL, and flavor preferences pertaining to underage JUUL users. OBJECTIVE: The aim of this study was to analyze social media data related to demographics, methods of access, product characteristics, and use patterns of underage JUUL use. METHODS: We collected publicly available JUUL-related data from Reddit. We extracted and summarized the age, location, and flavor preference of subreddit UnderageJuul users. We also compared common and unique users between subreddit UnderageJuul and subreddit JUUL. The methods of purchasing JUULs were analyzed by manually examining the content of the Reddit threads. RESULTS: A total of 716 threads and 2935 comments were collected from the subreddit UnderageJuul before it was shut down. Most threads did not mention a specific age, but ages ranged from 13 years to greater than 21 years in those that did. Mango, mint, and cucumber were the most popular among the 7 flavors listed on JUUL's official website, and 336 subreddit UnderageJuul threads mentioned 7 discreet approaches to circumvent relevant legal regulations to get JUUL products, the most common of which was purchasing JUUL from other Reddit users (n=181). Almost half of the UnderageJuul users (389/844, 46.1%) also participated in discussions on the main JUUL subreddit and sought information across multiple Reddit forums. Most (64/74, 86%) posters were from large metropolitan areas. CONCLUSIONS: The subreddit UnderageJuul functioned as a forum to explore methods of obtaining JUUL and to discuss and recommend specific flavors before it was shut down. About half of those using UnderageJuul also used the more general JUUL subreddit, so a forum still exists where youths can attempt to share information on how to obtain JUUL and other products. Exploration of such social media data in real time for rapid public health surveillance could provide early warning for significant health risks before they become major public health threats.

Mining online e-liquid reviews for opinion polarities about e-liquid features.

BACKGROUND: In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. METHODS: We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users' preference on this feature. RESULTS: The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. CONCLUSIONS: We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.

An Examination of Electronic Cigarette Content on Social Media: Analysis of E-Cigarette Flavor Content on Reddit.

In recent years, the emerging electronic cigarette (e-cigarette) marketplace has shown great development prospects all over the world. Reddit, one of the most popular forums in the world, has a very large user group and thus great influence. This study aims to gain a systematic understanding of e-cigarette flavors based on data collected from Reddit. Flavor popularity, mixing, characteristics, trends, and brands are analyzed. Fruit flavors were mentioned the most (n = 15,720) among all the posts and were among the most popular flavors (n = 2902) used in mixed blends. Strawberry and vanilla flavors were the most popular for e-juice mixing. The number of posts discussing e-cigarette flavors has increased sharply since 2014. Mt. Baker Vapor and Hangen were the most popular brands discussed among users. Information posted on Reddit about e-cigarette flavors reflected consumers' interest in a variety of flavors. Our findings suggest that Reddit could be used for data mining and analysis of e-cigarette-related content. Understanding how e-cigarette consumers' view and utilize flavors within their vaping experience and how producers and marketers use social media to promote flavors and sell products could provide valuable information for regulatory decision-makers.