Public interest and seasonal peaks for wisdom teeth related web inquiries - A google trends analysis.

Objective: The study aimed to analyze the public interest in wisdom teeth-related search terms as well as regional and seasonal trends based on information from the Google search engine. METHODS: With the help of the online search query tool, Google Trends, the public interest in the primary search term "wisdom teeth" for the timeframe between January 1st, 2004 and September 31st, 2021 was analyzed. To do so, a country-specific search was conducted in English-speaking countries (the USA, the UK, Canada, and Australia) in the northern and southern hemispheres. The extracted time series was examined for reliability, and a Cosinor analysis evaluated the statistical significance of seasonal interest peaks. RESULTS: The reliability of averaged time series data on the search term "wisdom teeth" was excellent in all examined countries. In all countries analyzed, "wisdom teeth removal" was one of the most common related search terms. Significant interest peaks for wisdom teeth-related search terms were found in Canada and the USA during summer (p < .001). In Canada and the USA, significant seasonal patterns with the highest interest during the summer months, could be displayed. CONCLUSION: This phenomenon could be caused by increased wisdom teeth-related complaints induced by seasonal climate changes.

When and in what circumstances is patient-targeted googling acceptable for health and social care professionals? A narrative review and thematic analysis.

Background: Patient-targeted Googling (PTG) is the use of Internet search engines by care professionals to source information about their patients. Objective: To thematically analyse research evidence on PTG and explain what, why and how it can be used for the benefit of patient care. Methods: The Scale for the Assessment of Narrative Review articles was used as a reporting tool. Studies were identified via AMED, CINAHL, MEDLINE and APA PsycInfo, ProQuest, and grey literature via Google Scholar. Results: 19 studies were included, and content was thematically analysed. Themes included practitioner behaviours, attitudes and experience, the nature of online information, when PTG is not acceptable, when, why and how is PTG acceptable and the need for education and training on PTG. Discussion & conclusion: In the absence of professional guidance, it makes practical recommendations about why and in what circumstances can use patient-targeted Googling for the benefit of patient care.

The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid).

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision. Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register. Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records. Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature.

Leveraging Google Trends to identify Indonesian tuberculosis trends before and after the implementation of a national mandatory notification system.

BACKGROUND: Tuberculosis (TB) burden and the underreporting of TB remain major health challenges in Indonesia. Interest in the internet is growing extensively, and the introduction of the TB mandatory electronic notification system in 2017 engaged the public's interest to leverage digital traces regarding TB information in Indonesia. OBJECTIVE: To quantify the correlation between Google Trends data and Indonesian TB surveillance data before and after the implementation of a mandatory TB notification system. METHODS: Google Trends searches on TB information were used. We used two sets of time series data, including before and after the launch of the TB notification system. Pearson's correlation was used to measure the correlation between TB search terms and official TB reports. RESULTS: The moving average graph showed a linear pattern of TB information with TB reports after 2017. Pearson's correlation estimated a high correlation for TB definition, TB symptoms, and official TB reports with an R-value range of 0.97 to -1.00 (p ≤ 0.05) and showed an increasing trend in TB information searching after 2016. CONCLUSION: Google Trends data can depict public interest in the TB epidemic. Validation of information-searching behavior is required to advocate the implementation of Google Trends for TB digital surveillance in Indonesia.

VAIV bio-discovery service using transformer model and retrieval augmented generation.

BACKGROUND: There has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery. MAIN BODY: We propose a novel biomedical neural search service called 'VAIV Bio-Discovery', which supports enhanced knowledge discovery and document search on unstructured text such as PubMed. It mainly handles with information related to chemical compound/drugs, gene/proteins, diseases, and their interactions (chemical compounds/drugs-proteins/gene including drugs-targets, drug-drug, and drug-disease). To provide comprehensive knowledge, the system offers four search options: basic search, entity and interaction search, and natural language search. We employ T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the interaction extraction task by removing the self-attention layer in the decoder block. It also assists in interpreting research findings by summarizing the retrieved search results for a given natural language query with Retrieval Augmented Generation (RAG). The search engine is built with a hybrid method that combines neural search with the probabilistic search, BM25. CONCLUSION: As a result, our system can better understand the context, semantics and relationships between terms within the document, enhancing search accuracy. This research contributes to the rapidly evolving biomedical field by introducing a new service to access and discover relevant knowledge.

Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking.

Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends.

Online Search Trends Related to Bariatric Surgery and Their Relationship with Utilization in Australia.

PURPOSE: There is an abundance of online information related to bariatric surgery. Patients may prefer a specific type of bariatric surgery based on what they read online. The primary aim of this study was to determine online search trends in bariatric surgery over time in Australia and worldwide. The secondary aim was to establish a relationship between public online search activity and the types of bariatric surgery performed in Australia. MATERIALS AND METHOD: The terms "adjustable gastric band," "sleeve gastrectomy," and "gastric bypass surgery" were submitted for search volume analysis in Australia and worldwide using the Google Trends "Topic" search function. This was compared alongside the numbers of gastric bandings, sleeve gastrectomies, and gastric bypass surgeries performed in Australia over time to determine if there was a relationship between the two. RESULTS: Search trends for "adjustable gastric band" and "sleeve gastrectomy" in Australia were similar to trends seen worldwide. However, search trends for "gastric bypass surgery" differ between Australia and the rest of the world. It took at least a year for online searches to reflect the higher number of sleeve gastrectomies performed relative to gastric bandings. There was a lag time of over four years before online searches reflected the higher number of gastric bypass surgery performed compared to gastric banding. CONCLUSION: Search interests in Australia and worldwide were similar for gastric banding and sleeve gastrectomy but different for gastric bypass surgery. Online search activity did not have a significant association with the types of bariatric surgery being performed in Australia.

Measuring vaccination coverage and concerns of vaccine holdouts from web search logs.

To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or not granular enough, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here we show how search engine logs and machine learning can help to fill these gaps, using anonymized Bing data from February to August 2021. First, we develop a vaccine intent classifier that accurately detects when a user is seeking the COVID-19 vaccine on Bing. Our classifier demonstrates strong agreement with CDC vaccination rates, while preceding CDC reporting by 1-2 weeks, and estimates more granular ZIP-level rates, revealing local heterogeneity in vaccine seeking. To study vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 67% likelier to click on untrusted news sites, and are much more concerned about vaccine requirements, development, and vaccine myths. Even within holdouts, clusters emerge with different concerns and openness to the vaccine. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators predict when individuals convert from holding out to seeking the vaccine.

Identifying sarcoidosis trends using web search and real-world data in Sweden: a retrospective longitudinal study.

Web search data are associated with disease incidence, population interest, and seasonal variations. This study aimed to investigate seasonal and geographical variations of web search data for sarcoidosis and to explore its association with external factors and sarcoidosis incidence in Sweden. Therefore, sarcoidosis-related data from Google Ads Keyword Planer (2017-2020) were generated for Sweden according to its 21 counties. The relationship between search volume and season, region, population demographics, environmental factors, and the sarcoidosis incidence listed in the National Patient Register was assessed. Analyses revealed seasonal variations for Sweden with an overall peak in the spring and autumn. Geographical differences were observed, with a higher search volume for north-western counties and the lowest search volume for Stockholm County. At the country level, the search volume was positively associated with the sarcoidosis incidence. Higher male proportion and older mean age were associated with a higher search volume, while a higher proportion of foreign-born residents, humidity, and mean temperature were associated with a lower search volume. Our analyses detected correlations between web search data, sarcoidosis incidence, and external factors. Analyses of sarcoidosis web search data therefore appear to be a valuable approach to disease surveillance to address medical needs and public interest.

Utilizing Artificial Intelligence-Based Tools for Addressing Clinical Queries: ChatGPT Versus Google Gemini.

BACKGROUND: Artificial intelligence (AI)-based text generators, such as ChatGPT (OpenAI) and Google Bard (now Google Gemini), have demonstrated proficiency in predicting words and providing responses to various questions. However, their performance in answering clinical queries has not been well assessed. This comparative analysis aimed to assess the capabilities of ChatGPT and Google Gemini in addressing clinical questions. METHOD: Separate interactions with ChatGPT and Google Gemini were conducted to obtain responses to the clinical question, posed in a PICOT (patient, intervention, comparison, outcome, time) format. To ascertain the accuracy of the information provided by the AI chat bots, a thorough examination of full-text articles was conducted. RESULTS: Although ChatGPT exhibited relative accuracy in generating bibliographic information, it displayed some inconsistencies in clinical content. Conversely, Google Gemini generated citations and summaries that were entirely fabricated. CONCLUSION: Despite generating responses that may appear credible, both AI-based tools exhibited factual inaccuracies, raising substantial concerns about their reliability as potential sources of clinical information. [J Nurs Educ. 2024;63(8):556-559.].

The Tromsø Study: Lessons Learnt About Searching for Information About Health and Illness.

INTRODUCTION: Norway has a high use of e-health. METHODS: This paper summarizes and discusses the published data from the Tromsø 7 Study, conducted between 2015 and 2016, focusing on e-health utilization in the Norwegian population aged 40 and above. RESULTS: More than half of the participants reported using the Internet for health purposes. The main channels for obtaining information were search engines, apps, social media platforms, and online videos. The respondents frequently acted upon the information obtained online, and online health information influenced decisions regarding healthcare utilization and treatment management. Most respondents indicated a positive reaction to the information found online. CONCLUSIONS: The Tromsø 7 Study highlights the widespread utilization of e-health in Norway. The study also emphasizes the significant impact of e-health on individuals' decision-making processes related to their health. The findings suggest that the use of e-health overall does not replace the use of traditional health services, but rather functions as a supplement. Most respondents report positive reactions to online health information, highlighting the importance and relevance of e-health in modern healthcare practices.

People also ask: How does this tool affect exploration-exploitation strategies with regard to prior domain knowledge and search context? An eye-tracking study.

With the diversification of Internet uses, online content type has become richer. Alongside organic results, search engine results pages now provide tools to improve information searching and learning. The People also ask (PAA) box is intended to reduce users' cognitive costs by offering easily accessible information. Nevertheless, there has been scant research on how users actually process it, compared with more traditional content type (i.e., organic results and online documents). The present eye-tracking study explored this question by considering the search context (complex lookup task vs. exploratory task) and users' prior domain knowledge (high vs. low). Main results show that users fixated the PAA box and online documents more to achieve exploratory goals, and fixated organic results more to achieve lookup goals. Users with low knowledge process PAA content at an early stage in their search contrary to their counterparts with high knowledge. Given these results, information system developers should diversify PAA content according to search context and users' prior domain knowledge.

Public interest in palliative care in Latin America: A Google Trends analysis.

The demand for palliative care is increasing globally, yet a notable lack of awareness continues to present a significant obstacle to its widespread adoption. The use of digital tools like Google Trends can help gauging public interest in specific topics. We used Google Trends to conduct a systematic search of terms related to palliative care from January 1, 2010, to May 10, 2023. The results were filtered by location, including worldwide and Latin American countries. We found a global increase in searches for terms related to palliative care, with a peak in December 2022 associated with the death of Brazilian footballer Pelé. Countries like Brazil, Mexico, and Colombia mirrored this trend, while others like Argentina and Peru did not. Interest in palliative care is on the rise in Latin America, albeit with notable regional variations.

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

AI-Assisted Processing Pipeline to Boost Protein Isoform Detection.

Proteomics, the study of proteins within biological systems, has seen remarkable advancements in recent years, with protein isoform detection emerging as one of the next major frontiers. One of the primary challenges is achieving the necessary peptide and protein coverage to confidently differentiate isoforms as a result of the protein inference problem and protein false discovery rate estimation challenge in large data. In this chapter, we describe the application of artificial intelligence-assisted peptide property prediction for database search engine rescoring by Oktoberfest, an approach that has proven effective, particularly for complex samples and extensive search spaces, which can greatly increase peptide coverage. Further, it illustrates a method for increasing isoform coverage by the PickedGroupFDR approach that is designed to excel when applied on large data. Real-world examples are provided to illustrate the utility of the tools in the context of rescoring, protein grouping, and false discovery rate estimation. By implementing these cutting-edge techniques, researchers can achieve a substantial increase in both peptide and isoform coverage, thus unlocking the potential of protein isoform detection in their studies and shedding light on their roles and functions in biological processes.

Association Between Prosuicide Website Searches Through Google and Suicide Death in the United States From 2010 to 2021: Lagged Time-Series Analysis.

BACKGROUND:  The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means. OBJECTIVE:  This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death. METHODS:  National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted. RESULTS:  Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests. CONCLUSIONS:  Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals.

Google Internet searches related to inflammatory arthritis: An observational study using Google Trends data.

OBJECTIVE: The Internet has transformed how patients access health information. We examined Google search engine data to understand which aspects of health are most often searched for in combination with inflammatory arthritis (IA). METHODS: Using Google Trends data (2011-2022) we determined the relative popularity of searches for 'patient symptoms' (pain, fatigue, stiffness, mood, work) and 'treat-to-target' (disease-modifying drugs, steroids, swelling, inflammation) health domains made with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (AxSpA) in the UK/USA. Google Trends normalises searches by popularity over time and region, generating 0-100 scale relative search volumes (RSV; 100 represents the time-point with most searches). Up to five search term combinations can be compared. RESULTS: In all IA forms, pain was the most popular patient symptom domain. UK/USA searches for pain gave mean RSVs of 58/79, 34/51, and 39/63 with RA, PsA, and AxSpA; mean UK/USA RSVs for other patient symptom domains ranged 2-7/2-8. Methotrexate was the most popular treat-to-target search term with RA/PsA in the UK (mean 28/21) and USA (mean 63/33). For AxSpA, inflammation was most popular (mean UK/USA 9/34). Searches for pain were substantially more popular than searches for methotrexate in RA and PsA, and inflammation in AxSpA. Searches increased over time. CONCLUSIONS: Pain is the most popular search term used with IA in Google searches in the UK/USA, supporting surveys/qualitative studies highlighting the importance of improving pain to patients with IA. Routine pain assessments should be embedded within treat-to-target strategies to ensure patient perspectives are considered.