VaxBot-HPV: A GPT-based Chatbot for Answering HPV Vaccine-related Questions.

Background: HPV vaccine is an effective measure to prevent and control the diseases caused by Human Papillomavirus (HPV). This study addresses the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine. Methods: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and testing purposes. To comprehensively understand the capabilities and potential of GPT-based chatbots, three models were involved in this study : GPT-3.5, VaxBot-HPV, and GPT-4. The evaluation criteria included answer relevancy and faithfulness. Results: VaxBot-HPV demonstrated superior performance in answer relevancy and faithfulness compared to baselines (Answer relevancy: 0.85; Faithfulness: 0.97) for the test questions in KB, (Answer relevancy: 0.85; Faithfulness: 0.96) for GPT generated questions. Conclusions: This study underscores the importance of leveraging advanced language models and fine-tuning techniques in the development of chatbots for healthcare applications, with implications for improving medical education and public health communication.

Translating motivational interviewing for the HPV vaccine into a computable ontology model for automated AI conversational interaction.

Human papillomavirus (HPV) vaccinations are lower than expected. To protect the onset of head and neck cancers, innovative strategies to improve the rates are needed. Artificial intelligence may offer some solutions, specifically conversational agents to perform counseling methods. We present our efforts in developing a dialogue model for automating motivational interviewing (MI) to encourage HPV vaccination. We developed a formalized dialogue model for MI using an existing ontology-based framework to manifest a computable representation using OWL2. New utterance classifications were identified along with the ontology that encodes the dialogue model. Our work is available on GitHub under the GPL v.3. We discuss how an ontology-based model of MI can help standardize/formalize MI counseling for HPV vaccine uptake. Our future steps will involve assessing MI fidelity of the ontology model, operationalization, and testing the dialogue model in a simulation with live participants.

RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.

OBJECTIVES: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND METHODS: RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. RESULTS: The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. CONCLUSION: By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.

Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning.

Background Existing studies on cardiovascular diseases (CVDs) often focus on individual-level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county-level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease). Methods and Results We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios. Conclusions The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.

Higher mortality among lean patients with non-alcoholic fatty liver disease despite fewer metabolic comorbidities.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) can develop in individuals who are not overweight. Whether lean persons with NAFLD have lower mortality and lower incidence of cirrhosis, cardiovascular diseases (CVD), diabetes mellitus (DM) and cancer than overweight/obese persons with NAFLD remains inconclusive. We compared mortality and incidence of cirrhosis, CVD, DM and cancer between lean versus non-lean persons with NAFLD. METHODS: This is a retrospective study of adults with NAFLD in a single centre from 2012 to 2021. Primary outcomes were mortality and new diagnosis of cirrhosis, CVD, DM and cancer. Outcomes were modelled using competing risk analysis and Cox proportional hazards regression analysis. RESULTS: A total of 18,594 and 13,420 patients were identified for cross-sectional and longitudinal analysis respectively: approximately 11% lean, 25% overweight, 28% class 1 obesity and 35% class 2-3 obesity. The median age was 51.0 years, 54.6% were women. The median follow-up was 49.3 months. Lean patients had lower prevalence of metabolic diseases at baseline and lower incidence of cirrhosis and DM than non-lean patients and no difference in CVD, any cancer or obesity-related cancer during follow-up. However, lean patients had significantly higher mortality with incidence per 1000 person-years of 16.67, 10.11, 7.37 and 8.99, respectively, in lean, overweight, obesity class 1 and obesity class 2-3 groups respectively. CONCLUSIONS: Lean patients with NAFLD had higher mortality despite lower incidence of cirrhosis and DM, and similar incidence of CVD and cancer and merit similar if not more attention as non-lean patients with NAFLD.

Identification of missing hierarchical relations in the vaccine ontology using acquired term pairs.

BACKGROUND: The Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applications that it is being used in. Quality assurance of VO is imperative to ensure that it provides accurate domain knowledge to these downstream tasks. Manual review to identify and fix quality issues (such as missing hierarchical is-a relations) is challenging given the complexity of the ontology. Automated approaches are highly desirable to facilitate the quality assurance of VO. METHODS: We developed an automated lexical approach that identifies potentially missing is-a relations in VO. First, we construct two types of VO concept-pairs: (1) linked; and (2) unlinked. Each concept-pair further derives an Acquired Term Pair (ATP) based on their lexical features. If the same ATP is obtained by a linked concept-pair and an unlinked concept-pair, this is considered to indicate a potentially missing is-a relation between the unlinked pair of concepts. RESULTS: Applying this approach on the 1.1.192 version of VO, we were able to identify 232 potentially missing is-a relations. A manual review by a VO domain expert on a random sample of 70 potentially missing is-a relations revealed that 65 of the cases were valid missing is-a relations in VO (a precision of 92.86%). CONCLUSIONS: The results indicate that our approach is highly effective in identifying missing is-a relation in VO.

Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence.

BACKGROUND: Automatic skin lesion recognition has shown to be effective in increasing access to reliable dermatology evaluation; however, most existing algorithms rely solely on images. Many diagnostic rules, including the 3-point checklist, are not considered by artificial intelligence algorithms, which comprise human knowledge and reflect the diagnosis process of human experts. OBJECTIVE: In this paper, we aimed to develop a semisupervised model that can not only integrate the dermoscopic features and scoring rule from the 3-point checklist but also automate the feature-annotation process. METHODS: We first trained the semisupervised model on a small, annotated data set with disease and dermoscopic feature labels and tried to improve the classification accuracy by integrating the 3-point checklist using ranking loss function. We then used a large, unlabeled data set with only disease label to learn from the trained algorithm to automatically classify skin lesions and features. RESULTS: After adding the 3-point checklist to our model, its performance for melanoma classification improved from a mean of 0.8867 (SD 0.0191) to 0.8943 (SD 0.0115) under 5-fold cross-validation. The trained semisupervised model can automatically detect 3 dermoscopic features from the 3-point checklist, with best performances of 0.80 (area under the curve [AUC] 0.8380), 0.89 (AUC 0.9036), and 0.76 (AUC 0.8444), in some cases outperforming human annotators. CONCLUSIONS: Our proposed semisupervised learning framework can help with the automatic diagnosis of skin disease based on its ability to detect dermoscopic features and automate the label-annotation process. The framework can also help combine semantic knowledge with a computer algorithm to arrive at a more accurate and more interpretable diagnostic result, which can be applied to broader use cases.

Using Machine Learning-Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions.

BACKGROUND: The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. OBJECTIVE: The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)-based methods. METHODS: Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. RESULTS: A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). CONCLUSIONS: ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

Nonselective beta-blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the United States.

BACKGROUND: Previous studies have demonstrated an association between nonselective beta-blockers (NSBBs) and lower risk of hepatocellular carcinoma (HCC) in cirrhosis. However, there has been no population-based study investigating the risk of HCC among cirrhotic patients treated using carvedilol. AIMS: To determine the risk of HCC among cirrhotic patients with NSBBs including carvedilol. METHODS: This retrospective cohort study utilised the Cerner Health Facts database in the United States from 2000 to 2017. Kaplan-Meier estimate, Cox proportional hazards regression, and propensity score matching (PSM) were used to test the HCC risk among the carvedilol, nadolol, and propranolol groups compared with no beta-blocker group. RESULTS: The final cohort comprised 107 428 eligible patients. The 100-month cumulative HCC incidence of NSBBs was significantly lower than the no beta-blocker group (carvedilol (11.24%) vs no beta-blocker (15.69%), nadolol (27.55%) vs no beta-blocker (32.11%), and propranolol (26.17%) vs no beta-blocker (28.84%) (P values < 0.0001). NSBBs were associated with a significantly lower risk of HCC (Hazard ratio: carvedilol 0.61 (95% CI 0.51-0.73), nadolol 0.74 (95% CI 0.63-0.87), propranolol 0.75 (95% CI 0.66-0.84) after PSM in the multivariate cox analysis. In subgroup analysis, NSBBs reduced the risk of HCC in cirrhosis with complications and non-alcoholic cirrhosis. CONCLUSIONS: NSBBs, including carvedilol, were associated with a significantly decreased risk of HCC in patients with cirrhosis when compared with no beta-blocker regardless of complications status. Future randomised-controlled studies comparing the incidence of HCC among NSBBs should elucidate which NSBB would be the best option to prevent HCC in cirrhosis.

Experimenting with a Prototype Interactive Narrative Game to Improve Knowledge and Beliefs for the HPV Vaccine.

Narratives can have a powerful impact on our health-related beliefs, attitudes, and behaviors. The human papillomavirus (HPV) vaccine can protect against human papillomavirus that leads to different types of cancers. However, HPV vaccination rates are low. This study explored the effectiveness of a narrative-based interactive game about the HPV vaccines as a method to communicate knowledge and perhaps create behavioral outcomes. We developed a serious storytelling game called Vaccination Vacation inspired by personal narratives of individuals who were impacted by the HPV. We tested the game using a randomized control study of 99 adult participants and compared the HPV knowledge and vaccine beliefs of the Gamer Group (who played the game, n = 44) and the Reader group (who read a vaccine information sheet, n = 55). We also evaluated the usability of the game. In addition to high usability, the interactive game slightly impacted the beliefs about the HPV vaccine over standard delivery of vaccine information, especially among those who never received the HPV vaccine. We also observed some gender-based differences in perception towards usability and the likelihood of frequently playing the game. A narrative-based game could bring positive changes to players' HPV-related health beliefs. The combination of more comprehensive HPV vaccine information with the narratives may produce a larger impact. Narrative-based games can be effectively used in other vaccine education interventions and warrant future research.

Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine.

Importance: Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies. Objective: To develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media. Design, Setting, and Participants: This retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018. Main Outcomes and Measures: The prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracted from their profiles, and geographic analyses were performed to identify the clustering of public perceptions of the HPV vaccine. Results: A total of 1 431 463 English-language posts from 486 116 unique usernames were collected. Deep learning algorithms achieved F-1 scores ranging from 0.6805 (95% CI, 0.6516-0.7094) to 0.9421 (95% CI, 0.9380-0.9462) in mapping discussions to the constructs of behavior change theories. LOESS revealed trends in constructs; for example, prevalence of perceived barriers, a construct of HBM, deceased from its apex in July 2015 (56.2%) to its lowest prevalence in October 2018 (28.4%; difference, 27.8%; P < .001); Positive attitudes toward the HPV vaccine, a construct of TPB, increased from early 2017 (30.7%) to 41.9% at the end of the study (difference, 11.2%; P < .001), while negative attitudes decreased from 42.3% to 31.3% (difference, 11.0%; P < .001) during the same period. Interstate variations in public perceptions of the HPV vaccine were also identified; for example, the states of Ohio and Maine showed a relatively high prevalence of perceived barriers (11 531 of 17 106 [67.4%] and 1157 of 1684 [68.7%]) and negative attitudes (9655 of 17 197 [56.1%] and 1080 of 1793 [60.2%]). Conclusions and Relevance: This cohort study provided a good understanding of public perceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level.

Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.

OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning. MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network. RESULTS: For logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction. DISCUSSION/CONCLUSION: In our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted.

Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.

BACKGROUND: Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists' efforts. We aimed to evaluate the classification sensitivity and specificity of deep learning models to classify skin tumors and psoriasis for Chinese population with a modest number of dermoscopic images. METHODS: We developed a convolutional neural network (CNN) based on two datasets from a consecutive series of patients who underwent the dermoscopy in the clinic of the Department of Dermatology, Peking Union Medical College Hospital, between 2016 and 2018, prospectively. In order to evaluate the feasibility of the algorithm, we used two datasets. Dataset I consisted of 7192 dermoscopic images for a multi-class model to differentiate three most common skin tumors and other diseases. Dataset II consisted of 3115 dermoscopic images for a two-class model to classify psoriasis from other inflammatory diseases. We compared the performance of CNN with 164 dermatologists in a reader study with 130 dermoscopic images. The experts' consensus was used as the reference standard except for the cases of basal cell carcinoma (BCC), which were all confirmed by histopathology. RESULTS: The accuracies of multi-class and two-class models were 81.49% ±â€Š0.88% and 77.02% ±â€Š1.81%, respectively. In the reader study, for the multi-class tasks, the diagnosis sensitivity and specificity of 164 dermatologists were 0.770 and 0.962 for BCC, 0.807 and 0.897 for melanocytic nevus, 0.624 and 0.976 for seborrheic keratosis, 0.939 and 0.875 for the "others" group, respectively; the diagnosis sensitivity and specificity of multi-class CNN were 0.800 and 1.000 for BCC, 0.800 and 0.840 for melanocytic nevus, 0.850 and 0.940 for seborrheic keratosis, 0.750 and 0.940 for the "others" group, respectively. For the two-class tasks, the sensitivity and specificity of dermatologists and CNN for classifying psoriasis were 0.872 and 0.838, 1.000 and 0.605, respectively. Both the dermatologists and CNN achieved at least moderate consistency with the reference standard, and there was no significant difference in Kappa coefficients between them (P > 0.05). CONCLUSIONS: The performance of CNN developed with relatively modest number of dermoscopic images of skin tumors and psoriasis for Chinese population is comparable with 164 dermatologists. These two models could be used for screening in patients suspected with skin tumors and psoriasis respectively in primary care hospital.

Examining Potential Usability and Health Beliefs Among Young Adults Using a Conversational Agent for HPV Vaccine Counseling.

The human papillomavirus (HPV) vaccine is the most effective way to prevent HPV-related cancers. Integrating provider vaccine counseling is crucial to improving HPV vaccine completion rates. Automating the counseling experience through a conversational agent could help improve HPV vaccine coverage and reduce the burden of vaccine counseling for providers. In a previous study, we tested a simulated conversational agent that provided HPV vaccine counseling for parents using the Wizard of OZ protocol. In the current study, we assessed the conversational agent among young college adults (n=24), a population that may have missed the HPV vaccine during their adolescence when vaccination is recommended. We also administered surveys for system and voice usability, and for health beliefs concerning the HPV vaccine. Participants perceived the agent to have high usability that is slightly better or equivalent to other voice interactive interfaces, and there is some evidence that the agent impacted their beliefs concerning the harms, uncertainty, and risk denials for the HPV vaccine. Overall, this study demonstrates the potential for conversational agents to be an impactful tool for health promotion endeavors.

iDISK: the integrated DIetary Supplements Knowledge base.

OBJECTIVE: To build a knowledge base of dietary supplement (DS) information, called the integrated DIetary Supplement Knowledge base (iDISK), which integrates and standardizes DS-related information from 4 existing resources. MATERIALS AND METHODS: iDISK was built through an iterative process comprising 3 phases: 1) establishment of the content scope, 2) development of the data model, and 3) integration of existing resources. Four well-regarded DS resources were integrated into iDISK: The Natural Medicines Comprehensive Database, the "About Herbs" page on the Memorial Sloan Kettering Cancer Center website, the Dietary Supplement Label Database, and the Natural Health Products Database. We evaluated the iDISK build process by manually checking that the data elements associated with 50 randomly selected ingredients were correctly extracted and integrated from their respective sources. RESULTS: iDISK encompasses a terminology of 4208 DS ingredient concepts, which are linked via 6 relationship types to 495 drugs, 776 diseases, 985 symptoms, 605 therapeutic classes, 17 system organ classes, and 137 568 DS products. iDISK also contains 7 concept attribute types and 3 relationship attribute types. Evaluation of the data extraction and integration process showed average errors of 0.3%, 2.6%, and 0.4% for concepts, relationships and attributes, respectively. CONCLUSION: We developed iDISK, a publicly available standardized DS knowledge base that can facilitate more efficient and meaningful dissemination of DS knowledge.

Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks.

The human papillomavirus (HPV) vaccine protects adolescents and young adults from 9 high-risk HPV virus types that cause 90% of cervical and anal cancers and 70% of oropharyngeal cancers. This study extends our previous research analyzing online content concerning the HPV vaccination in social media platforms used by young adults, in which we used Pathfinder network scaling and methods of distributional semantics to characterize differences in knowledge organization reflected in consumer- and expert-generated online content. The current study extends this approach to evaluate HPV vaccine perceptions among young adults who populate Reddit, a major social media platform. We derived Pathfinder networks from estimates of semantic relatedness obtained by learning word embeddings from Reddit posts and compared these to networks derived from human expert estimation of the relationship between key concepts. Results revealed that users of Reddit, predominantly comprising young adults in the vaccine catch up age-group 18 through 26 years of age, perceived the HPV vaccine domain from a virus-framed perspective that could impact their lifestyle choices and that their awareness of the HPV vaccine for cancer prevention is also lacking. Further differences in knowledge structures were elucidated, with implications for future health communication initiatives.

Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States.

OBJECTIVES: The study sought to test the feasibility of using Twitter data to assess determinants of consumers' health behavior toward human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). MATERIALS AND METHODS: We used 3 Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers' discussions. We applied topic modeling to discover major themes and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS). RESULTS: We collected 2 846 495 tweets and analyzed 335 681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is "cervical cancer screening"; while in promotional tweets, the most popular topic is to increase awareness of "HPV causes cancer." A total of 87 of the 122 topics are correlated between promotional information and consumers' discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. Thirty-five topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions. CONCLUSIONS: Mining Twitter to assess consumers' health behaviors can not only obtain results comparable to surveys, but also yield additional insights via a theory-driven approach. Limitations exist; nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape.

Conceiving an application ontology to model patient human papillomavirus vaccine counseling for dialogue management.

BACKGROUND: In the United States and parts of the world, the human papillomavirus vaccine uptake is below the prescribed coverage rate for the population. Some research have noted that dialogue that communicates the risks and benefits, as well as patient concerns, can improve the uptake levels. In this paper, we introduce an application ontology for health information dialogue called Patient Health Information Dialogue Ontology for patient-level human papillomavirus vaccine counseling and potentially for any health-related counseling. RESULTS: The ontology's class level hierarchy is segmented into 4 basic levels - Discussion, Goal, Utterance, and Speech Task. The ontology also defines core low-level utterance interaction for communicating human papillomavirus health information. We discuss the design of the ontology and the execution of the utterance interaction. CONCLUSION: With an ontology that represents patient-centric dialogue to communicate health information, we have an application-driven model that formalizes the structure for the communication of health information, and a reusable scaffold that can be integrated for software agents. Our next step will to be develop the software engine that will utilize the ontology and automate the dialogue interaction of a software agent.

Investigating safety profiles of human papillomavirus vaccine across group differences using VAERS data and MedDRA.

BACKGROUND: The safety of vaccines is a critical factor in maintaining public trust in national vaccination programs. This study aimed to evaluate the safety profiles of human papillomavirus (HPV) vaccines with regard to the distribution of adverse events (AE) across gender and age, and the correlations across various AEs using the Food and Drug Administration/Centers for Disease Control and Prevention Vaccine Adverse Event Reporting System (VAERS). METHODS: For analyses, 27,348 patients aged between 9 and 25 years old with at least one AE reported in VAERS between the year of 2006 and 2017 were included. AEs were summarized into two levels: the lower level preferred term (PT) and higher level system organ classes (SOCs) based on the structure of Medical Dictionary for Regulatory Activities (MedDRA). A series of statistical analyses were applied on both levels of AEs. Zero-truncated Poisson regression and multivariate logistic regression models were first developed to assess the rate and risk of SOCs across age groups and genders. Pairwise Pearson correlation analyses and hierarchical clustering analyses were then conducted to explore the interrelationships and clustering pattern among AEs. RESULTS: We identified 27,337 unique HPV vaccine reports between 2006 and 2017. Disproportional reporting of AEs was observed across age and gender in 21 SOCs (p < 0.05). The correlation analyses found most SOCs demonstrate weak positive correlations except for five pairs which were negatively correlated: skin and subcutaneous tissue disorders + injury poisoning and procedural complications; skin and subcutaneous tissue disorders + nervous system disorders; Skin and subcutaneous tissue disorders + pregnancy, puerperium and perinatal conditions; nervous system disorders + pregnancy, puerperium and perinatal conditions; pregnancy, puerperium and perinatal conditions + general disorders and administration site conditions. Nervous system disorders had the most AEs which contributed to 12,448 (46%) cases. In the further analyses of correlations between PT in nervous system disorders, the three most strongly correlated AEs were psychiatric disorders (r = 0.35), gastrointestinal disorders (r = 0.215), and musculoskeletal and connective tissue disorders (r = 0.261). We observed an inter-SOCs correlation of the PTs among AE pairs by nervous system disorders/psychiatric disorders/gastrointestinal disorders/musculoskeletal and connective tissue disorders. CONCLUSIONS: The analyses revealed a different distribution pattern of AEs across gender and age subgroups in 21 SOC level AEs. Correlation analyses and hierarchical clustering analyses further revealed several correlated patterns across various AEs. However, findings from this study should be interpreted with caution. Further clinical studies are needed to understand the heterogeneity of AEs reporting across subgroups and the biological pathways among the statistically correlated AEs.