BACKGROUND: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language - which holds true for either structured or unstructured data. Medical data might encompass both types, with valuable information present in one type but not the other.
METHODS: The TOP Framework provides clinical practitioners as well as researchers with a unified framework for querying diverse data types and, furthermore, facilitates an easier and intuitive approach. Additionally, it supports collaboration on query modeling and sharing.
RESULTS: Having demonstrated its effectiveness with structured data, we introduce the integration of a component for unstructured data, specifically medical documents.
CONCLUSIONS: Our proof-of-concept shows a query language agnostic framework to model search queries for unstructured and structured data.

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.

BACKGROUND: 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.

BACKGROUND: There has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery.
METHODS: 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.
CONCLUSIONS: 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.

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.

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.

OBJECTIVE: 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.
METHODS: 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.
CONCLUSIONS: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.].

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.
CONCLUSIONS: 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.

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.