Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. OBJECTIVE: Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years. METHODS: Using Arksey and O'Malley's 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as "mobile healthcare," "wearable medical sensors," "smartphones", and "AI." We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain. RESULTS: We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research. CONCLUSIONS: The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

User Privacy, Surveillance and Public Health during COVID-19 - An Examination of Twitterverse.

Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects-contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild.

Emergent pacemaker placement in a patient with Lyme carditis-induced complete heart block and ventricular asystole.

We report a case of a 31-year-old man who presented to the emergency department after four episodes of syncope within a 24 h time span. He was found to have symptomatic complete heart block associated with episodes of ventricular asystole lasting 5-6 s. He underwent emergent permanent pacemaker insertion during which he was found to have no underlying rhythm. He was later found to have positive serologies for Lyme disease despite no known exposure to ticks and neither signs nor symptoms of the disease. The pacemaker was ultimately removed due to resolution of his heart block with antibiotic therapy.

Reliability of early cortical auditory gamma-band responses.

OBJECTIVE: To evaluate the test-retest reliability of event-related power changes in the 30-150 Hz gamma frequency range occurring in the first 150 ms after presentation of an auditory stimulus. METHODS: Repeat intracranial electrocorticographic (ECoG) recordings were performed with 12 epilepsy patients, at ≥1-day intervals, using a passive odd-ball paradigm with steady-state tones. Time-frequency matching pursuit analysis was used to quantify changes in gamma-band power relative to pre-stimulus baseline. Test-retest reliability was estimated based on within-subject comparisons (paired t-test, McNemar's test) and correlations (Spearman rank correlations, intra-class correlations) across sessions, adjusting for within-session variability. Reliability estimates of gamma-band response robustness, spatial concordance, and reproducibility were compared with corresponding measurements from concurrent auditory evoked N1 responses. RESULTS: All patients showed increases in gamma-band power, 50-120 ms post-stimulus onset, that were highly robust across recordings, comparable to the evoked N1 responses. Gamma-band responses occurred regardless of patients' performance on behavioral tests of auditory processing, medication changes, seizure focus, or duration of test-retest interval. Test-retest reproducibility was greatest for the timing of peak power changes in the high-gamma range (65-150 Hz). Reliability of low-gamma responses and evoked N1 responses improved at higher signal-to-noise levels. CONCLUSIONS: Early cortical auditory gamma-band responses are robust, spatially concordant, and reproducible over time. SIGNIFICANCE: These test-retest ECoG results confirm the reliability of auditory gamma-band responses, supporting their utility as objective measures of cortical processing in clinical and research studies.

Impact of a recent lead recall on utilization of implantable cardioverter defibrillators: data from the NCDR ICD Registry™.

INTRODUCTION: Underutilization of ICDs is well documented. It has been hypothesized that device recalls, and the resultant negative publicity, may contribute. METHODS AND RESULTS: To determine if the October 2007 recall of the Medtronic Fidelis lead was associated with a decrease in volume of ICD procedures in the United States, we analyzed data submitted to the ICD Registry™ between July 2006 and December 2008. Time-series analyses were performed comparing actual and predicted implant volumes following the recall, using monthly data from July 2006 to September 2007 to establish a trend line. Observed data points falling outside the 95% CIs from the trend line were considered statistically significant. The study cohort includes 173,616 implantations in 658 hospitals. Before October 2007, an average of 5,952 devices, 4,910 for primary prevention, were implanted per month. Following the recall, the average monthly number of implants was modestly lower at 5,623 (P = 0.05), 4,601 for primary prevention (P = 0.01.) However, as volume was decreasing prior, in time-series analysis, the observed monthly implant volume for primary prevention devices differed from expected based on the trend line for only 1 month. The proportion of Medtronic implants declined from 51.1% in the 15 months prior to the recall to 45.8% in the 15 months of the recall or after (P < 0.01), falling outside the 95% CI of the trend line for 3 months in time-series analysis. CONCLUSIONS: A recent well-publicized lead recall had minimal impact on ICD utilization either overall or for primary prevention.