Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p =0.774 under a paired t-test). Conclusions . PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare.

Background: The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators. Objective: This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare. Methods: We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns. Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research. Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.

Wafer-Scale Periodic Poling of Thin-Film Lithium Niobate.

Periodically poled lithium niobate on insulator (PPLNOI) offers an admirably promising platform for the advancement of nonlinear photonic integrated circuits (PICs). In this context, domain inversion engineering emerges as a key process to achieve efficient nonlinear conversion. However, periodic poling processing of thin-film lithium niobate has only been realized on the chip level, which significantly limits its applications in large-scale nonlinear photonic systems that necessitate the integration of multiple nonlinear components on a single chip with uniform performances. Here, we demonstrate a wafer-scale periodic poling technique on a 4-inch LNOI wafer with high fidelity. The reversal lengths span from 0.5 to 10.17 mm, encompassing an area of ~1 cm2 with periods ranging from 4.38 to 5.51 µm. Efficient poling was achieved with a single manipulation, benefiting from the targeted grouped electrode pads and adaptable comb line widths in our experiment. As a result, domain inversion is ultimately implemented across the entire wafer with a 100% success rate and 98% high-quality rate on average, showcasing high throughput and stability, which is fundamentally scalable and highly cost-effective in contrast to traditional size-restricted chiplet-level poling. Our study holds significant promise to dramatically promote ultra-high performance to a broad spectrum of applications, including optical communications, photonic neural networks, and quantum photonics.

Examining Online Behaviors of Adult-Child and Spousal Caregivers for People Living With Alzheimer Disease or Related Dementias: Comparative Study in an Open Online Community.

BACKGROUND: Alzheimer disease or related dementias (ADRD) are severe neurological disorders that impair the thinking and memory skills of older adults. Most persons living with dementia receive care at home from their family members or other unpaid informal caregivers; this results in significant mental, physical, and financial challenges for these caregivers. To combat these challenges, many informal ADRD caregivers seek social support in online environments. Although research examining online caregiving discussions is growing, few investigations have distinguished caregivers according to their kin relationships with persons living with dementias. Various studies have suggested that caregivers in different relationships experience distinct caregiving challenges and support needs. OBJECTIVE: This study aims to examine and compare the online behaviors of adult-child and spousal caregivers, the 2 largest groups of informal ADRD caregivers, in an open online community. METHODS: We collected posts from ALZConnected, an online community managed by the Alzheimer's Association. To gain insights into online behaviors, we first applied structural topic modeling to identify topics and topic prevalence between adult-child and spousal caregivers. Next, we applied VADER (Valence Aware Dictionary for Sentiment Reasoning) and LIWC (Linguistic Inquiry and Word Count) to evaluate sentiment changes in the online posts over time for both types of caregivers. We further built machine learning models to distinguish the posts of each caregiver type and evaluated them in terms of precision, recall, F1-score, and area under the precision-recall curve. Finally, we applied the best prediction model to compare the temporal trend of relationship-predicting capacities in posts between the 2 types of caregivers. RESULTS: Our analysis showed that the number of posts from both types of caregivers followed a long-tailed distribution, indicating that most caregivers in this online community were infrequent users. In comparison with adult-child caregivers, spousal caregivers tended to be more active in the community, publishing more posts and engaging in discussions on a wider range of caregiving topics. Spousal caregivers also exhibited slower growth in positive emotional communication over time. The best machine learning model for predicting adult-child, spousal, or other caregivers achieved an area under the precision-recall curve of 81.3%. The subsequent trend analysis showed that it became more difficult to predict adult-child caregiver posts than spousal caregiver posts over time. This suggests that adult-child and spousal caregivers might gradually shift their discussions from questions that are more directly related to their own experiences and needs to questions that are more general and applicable to other types of caregivers. CONCLUSIONS: Our findings suggest that it is important for researchers and community organizers to consider the heterogeneity of caregiving experiences and subsequent online behaviors among different types of caregivers when tailoring online peer support to meet the specific needs of each caregiver group.

Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Statements with Varying Degrees of Misinformation.

Background: There are many myths regarding Alzheimer's disease (AD) that have been circulated on the Internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models such as ChatGPT, may be a useful tool to help assess these myths for veracity and inaccuracy. However, they can induce misinformation as well. The objective of this study is to assess ChatGPT's ability to identify and address AD myths with reliable information. Methods: We conducted a cross-sectional study of clinicians' evaluation of ChatGPT (GPT 4.0)'s responses to 20 selected AD myths. We prompted ChatGPT to express its opinion on each myth and then requested it to rephrase its explanation using a simplified language that could be more readily understood by individuals with a middle school education. We implemented a survey using Redcap to determine the degree to which clinicians agreed with the accuracy of each ChatGPT's explanation and the degree to which the simplified rewriting was readable and retained the message of the original. We also collected their explanation on any disagreement with ChatGPT's responses. We used five Likert-type scale with a score ranging from -2 to 2 to quantify clinicians' agreement in each aspect of the evaluation. Results: The clinicians (n=11) were generally satisfied with ChatGPT's explanations, with a mean (SD) score of 1.0(±0.3) across the 20 myths. While ChatGPT correctly identified that all the 20 myths were inaccurate, some clinicians disagreed with its explanations on 7 of the myths.Overall, 9 of the 11 professionals either agreed or strongly agreed that ChatGPT has the potential to provide meaningful explanations of certain myths. Conclusions: The majority of surveyed healthcare professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation. However, the need for more refined and detailed explanations of the disease's mechanisms and treatments was highlighted.

Clinic choice of long or short segment pedicle screw-rod fixation in the treatment of thoracolumbar burst fracture: From scan data to numerical study.

Based on computerized tomography scanning images of human lumbar vertebrae, finite element (FE) analysis is performed to predict the stress of pedicle screws, rods, and fractured vertebra as well as the displacement of fractured vertebra after internal fixation treatment of thoracolumbar burst fracture. A three-dimensional FE model of L1-L5 lumbar vertebrae with L3 burst fracture has been established and four fixation methods, namely, short segment cross- and trans-injured vertebrae, long segment cross- and trans-injured vertebrae fixations, have been adopted to perform posterior pedicle fixation. The stress distributions of the screws, rods, and fractured vertebra and the total deformation of the fractured vertebra are investigated under six different physiological motions. From the view of the stress on the screw-rod system and the deformation of the fractured vertebral body, the long segment cross-injured vertebra fixation has the best mechanical performance, followed by the long segment trans-injured vertebra fixation, and then the short segment fixation trans-injured vertebra. The short segment fixation cross-injured vertebra performs the worst. Among the six motions, the forward flexion movement has the greatest impact on the screw-rod system and the fractured vertebra. However, the rotation motion greatly affects the stress of the screw in the long segment fixation. This indicates that the longer the fixed segment is, the more susceptible it is to human rotation. Thus, for patients with severe fracture, the long segment cross-injured vertebra is preferred. On the contrary, the short segment trans-injured vertebra fixation is optimal.

The Hidden Patient Connections: Predicting Hormonal Therapy Medication Discontinuation Using Hypergraph Neural Network on Clinical Communications.

Hormonal therapy is an important adjuvant treatment for breast cancer patients, but medication discontinuation of such therapy is not uncommon. The goal of this paper is to conduct research on the modeling of clinic communications, which have shown value in understanding medication discontinuation, to predict the discontinuation of hormonal therapy medications. Notably, we leveraged the Hypergraph Neural Network to capture the hidden connections of patients that were inferred from clinical communications. Combining the content of clinical communications as well as the demographics, insurance, and cancer stage information, our model achieved an AUC of 67.9%, which significantly outperformed other baselines such as Graph Convolutional Network (65.3%), Random Forest (62.7%), and Support Vector Machine (62.8%). Our study suggested that incorporating the hidden patient connections encoded in clinical communications into prediction models could boost their performance. Future research would consider combining structured medical records and clinical communications to better predict medication discontinuation.

A game theoretic approach to balance privacy risks and familial benefits.

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users' valuations of the service will affect a population's behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data.

Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study.

BACKGROUND: By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics. OBJECTIVE: This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. METHODS: We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. RESULTS: We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. CONCLUSIONS: This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

Characterization of Adult Patients with Autism Spectrum Disorder that Use Patient Portal.

Patients with autism spectrum disorder (ASD) access healthcare frequently, yet little is known about their interactions with patient portals. To describe adults with ASD using patient portal, we conducted regression analyses of visit history, demographics, co-occurring conditions and diagnoses, and patient portal use to determine factors most indicative of whether a patient 1) has sent at least one message (via patient or proxy) and 2) has at least one message sent on their behalf via a proxy account after they turned 18 years old. The 2,412-person cohort had 996 (41.3%) patients who had sent at least one message on their account with 129 (5.3%) of patients having at least one proxy message. This study found that adults with ASD are less likely to use messaging functionality and more likely to have a message sent via proxy than other patient populations. Comorbid mental illness was correlated with using messaging functionality.

Fatigue, Pain, and Medication: Mining Online Posts Regarding Rheumatoid Arthritis From Reddit.

Rheumatoid arthritis (RA), a chronic and systemic autoimmune disease that primarily attacks the joints around the body, is affecting a large number of people worldwide through severe symptoms and complications. Therefore, it is crucial to understand these patients' problems and support needs such that effective strategies or solutions can be made to improve their long-term treatment experience. In this paper, we present an in-depth study that is based on the structural topic model to uncover the themes and concerns in online RA posts from Reddit, an American social news aggregation, content rating, and discussion website. In addition, we compared the topic prevalence differences before and after the COVID-19 pandemic to understand the impact of the pandemic on these online users. This study demonstrates the potential of using text-mining techniques on social media data to learn the treatment experiments of RA patients.

Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

Predicting Hormonal Therapy Medication Discontinuation for Breast Cancer Patients using Structured Data in Electronic Medical Records.

Hormonal therapy (HT) reduces the risk of cancer recurrence and the mortality rate for patients with hormone-receptor-positive breast cancer. However, it is estimated that half of the patients fail to complete the standard 5-year adjuvant treatment protocol. We investigate the extent to which certain types of structured data in electronic medical records (EMRs), namely conditions, drugs, laboratory tests and procedures, as well as when such data is entered EMRs, can forecast HT discontinuation. Our experiments with EMR data from 2,251 patients showed that machine learning models based on these data types achieve fair performance (AUC of 0.65). More importantly, the performance was not statistically significantly different when fitting a model using all or only one feature type, suggesting that the model is robust to missing information in the EMR.

"Please Advise": Understanding the Needs of Informal Caregivers of People with Alzheimer's Disease and Related Dementia from Online Communities Through a Structured Topic Modeling Approach.

The informal or family caregivers of the Alzheimer's disease or related dementia (ADRD) patients, also known as the "invisible second patients", are often reported experiencing emotional and behavioral hardships. In recent years, the rapid development of online communities provides these caregivers a new opportunity for seeking information and emotional support. Comparing with offline social support services which have been constrained during the COVID-19 pandemic, online support allows caregivers to reach many peers in a convenient manner. This research aimed to examine the issues faced by ADRD caregivers through performing a structural topic modeling on posts from two online communities. Results revealed that the top concerns of the caregivers include getting along with Alzheimer's patients, family issues, patients' internal medical issues, stages of the disease, care facilities, etc. The results may have a further implication to the future implementation of psychological and social intervention of ADRD family care.

Mining Medication Use Patterns from Clinical Notes for Breast Cancer Patients Through a Two-Stage Topic Modeling Approach.

Obtaining medication use and response information is essential for both care providers and researchers to understand patients' medication use and long-term treatment patterns. While unstructured clinical notes contain such information, they have rarely been analyzed for this purpose on a large scale due to the demands of expensive manual reviews. Here, we aimed to extract and analyze medication use patterns from clinical notes for a population of breast cancer patients at an academic medical center using unsupervised topic modeling techniques. Notably, we proposed a two-stage modeling process that was built upon correlated topic modeling (CTM) and structural topic modeling (STM) to capture nuanced information about medication behavior, including drug-disease relationships as well as medication schedules. The STM-derived topics show longitudinal prevalence patterns that may reflect changing patient needs and behaviors after the diagnosis of a severe disease. The patterns also show promise as a predictor for medication-taking behavior.

Effect of Stellate Ganglion Block Combined with Lidocaine at Different Concentrations for Preemptive Analgesia on Postoperative Pain Relief and Adverse Reactions of Patients Undergoing Laparoscopic Cholecystectomy.

Objective: To explore the effect of stellate ganglion block (SGB) combined with lidocaine at different concentrations for preemptive analgesia on postoperative pain relief and adverse reactions of patients undergoing laparoscopic cholecystectomy (LC). Methods: Ninety patients undergoing LC in our hospital from June 2019 to June 2020 were selected as the subjects and were randomly divided into group A (30 cases), group B (30 cases), and group C (30 cases), all patients received SGB, and 10 mL of lidocaine at concentrations of 0.25%, 0.5%, and 0.75% was, respectively, administered to patients in groups A, B, and C, so as to compare the analgesic effect, adverse reactions, and clinical indicators among the three groups. Results: At T 1 and T 2, group C obtained obviously lower NRS scores than groups A and B (P < 0.001); compared with groups A and B, group A had obviously higher onset time (P < 0.001) and significantly lower duration (P < 0.001); no obvious differences in the hemodynamic indexes among the groups were observed (P > 0.05); group C obtained obviously higher BCS score than groups A and B; and the total incidence rate of adverse reactions was obviously higher in group C than in groups A and B (P < 0.05). Conclusion: Performing SGB combined with 0.5% lidocaine to patients undergoing LC achieves the optimal analgesic effect; such anesthesia plan can effectively stabilize patients' hemodynamics, present higher safety, and promote the regulation of the body internal environment. Further research will be conducive to establishing a better anesthesia plan for such patients.

The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study.

BACKGROUND: In November 2018, a Chinese researcher reported that his team had applied clustered regularly interspaced palindromic repeats or associated protein 9 to delete the gene C-C chemokine receptor type 5 from embryos and claimed that the 2 newborns would have lifetime immunity from HIV infection, an event referred to as #GeneEditedBabies on social media platforms. Although this event stirred a worldwide debate on ethical and legal issues regarding clinical trials with embryonic gene sequences, the focus has mainly been on academics and professionals. However, how the public, especially stratified by geographic region and culture, reacted to these issues is not yet well-understood. OBJECTIVE: The aim of this study is to examine web-based posts about the #GeneEditedBabies event and characterize and compare the public's stance across social media platforms with different user bases. METHODS: We used a set of relevant keywords to search for web-based posts in 4 worldwide or regional mainstream social media platforms: Sina Weibo (China), Twitter, Reddit, and YouTube. We applied structural topic modeling to analyze the main discussed topics and their temporal trends. On the basis of the topics we found, we designed an annotation codebook to label 2000 randomly sampled posts from each platform on whether a supporting, opposing, or neutral stance toward this event was expressed and what the major considerations of those posts were if a stance was described. The annotated data were used to compare stances and the language used across the 4 web-based platforms. RESULTS: We collected >220,000 posts published by approximately 130,000 users regarding the #GeneEditedBabies event. Our results indicated that users discussed a wide range of topics, some of which had clear temporal trends. Our results further showed that although almost all experts opposed this event, many web-based posts supported this event. In particular, Twitter exhibited the largest number of posts in opposition (701/816, 85.9%), followed by Sina Weibo (968/1140, 84.91%), Reddit (550/898, 61.2%), and YouTube (567/1078, 52.6%). The primary opposing reason was rooted in ethical concerns, whereas the primary supporting reason was based on the expectation that such technology could prevent the occurrence of diseases in the future. Posts from these 4 platforms had different language uses and patterns when they expressed stances on the #GeneEditedBabies event. CONCLUSIONS: This research provides evidence that posts on web-based platforms can offer insights into the public's stance on gene editing techniques. However, these stances vary across web-based platforms and often differ from those raised by academics and policy makers.

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.

OBJECTIVE: Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data. MATERIALS AND METHODS: The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework's effectiveness in maintaining the PK11 threshold of 0.01. RESULTS: When sharing COVID-19 county-level case data across all US counties, the framework's approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%. CONCLUSION: Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.

Implicit Incentives Among Reddit Users to Prioritize Attention Over Privacy and Reveal Their Faces When Discussing Direct-to-Consumer Genetic Test Results: Topic and Attention Analysis.

Background: As direct-to-consumer genetic testing services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so anonymously, but more recently, they have included face images when discussing their results. Various studies have shown that sharing images on social media tends to elicit more replies. However, users who do this forgo their privacy. When these images truthfully represent a user, they have the potential to disclose that user's identity. Objective: This study investigates the face image sharing behavior of direct-to-consumer genetic testing users in an online environment to determine if there exists an association between face image sharing and the attention received from other users. Methods: This study focused on r/23andme, a subreddit dedicated to discussing direct-to-consumer genetic testing results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to characterize the association between the attention that a post received, in terms of the number of comments, the karma score (defined as the number of upvotes minus the number of downvotes), and whether the post contained a face image. Results: We collected over 15,000 posts from the r/23andme subreddit, published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals revealing their faces by early 2020. The topics in posts including a face were primarily about sharing, discussing ancestry composition, or sharing family reunion photos with relatives discovered via direct-to-consumer genetic testing. On average, posts including a face image received 60% (5/8) more comments and had karma scores 2.4 times higher than other posts. Conclusions: Direct-to-consumer genetic testing consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate this risk, platform organizers and moderators could inform users about the risk of posting face images in a direct, explicit manner to make it clear that their privacy may be compromised if personal images are shared.

Using game theory to thwart multistage privacy intrusions when sharing data.

Person-specific biomedical data are now widely collected, but its sharing raises privacy concerns, specifically about the re-identification of seemingly anonymous records. Formal re-identification risk assessment frameworks can inform decisions about whether and how to share data; current techniques, however, focus on scenarios where the data recipients use only one resource for re-identification purposes. This is a concern because recent attacks show that adversaries can access multiple resources, combining them in a stage-wise manner, to enhance the chance of an attack's success. In this work, we represent a re-identification game using a two-player Stackelberg game of perfect information, which can be applied to assess risk, and suggest an optimal data sharing strategy based on a privacy-utility tradeoff. We report on experiments with large-scale genomic datasets to show that, using game theoretic models accounting for adversarial capabilities to launch multistage attacks, most data can be effectively shared with low re-identification risk.