Notes

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Automated texting platforms have emerged as a tool to facilitate communication between patients and health care providers with variable effects on achieving target blood pressure (BP). see more Understanding differences in the way patients interact with these communication platforms can inform their use and design for hypertension management.

Our primary aim was to explore the unique phenotypes of patient interactions with an automated text messaging platform for BP monitoring. Our secondary aim was to estimate associations between interaction phenotypes and BP control.

This study was a secondary analysis of data from a randomized controlled trial for adults with poorly controlled hypertension. A total of 201 patients with established primary care were assigned to the automated texting platform; messages exchanged throughout the 4-month program were analyzed. We used the k-means clustering algorithm to characterize two different interaction phenotypes program conformity and engagement style. First, we identifiedes may be useful for tailoring future automated texting interactions and designing future interventions to achieve better BP control.
The number of medical and health apps in the App Store and Google Play repositories has been increasing in the recent years, and most of these apps are in English. However, little is known about the domain of Spanish health apps and their evolution.

The aim of this study was to perform a retrospective descriptive analysis of medical apps for patients in the Spanish language by using Google search tools over a 5-year period and to compare the results by using a reproducible methodology to obtain a better knowledge of the medical apps available in the Spanish Language.

Over a 5-year period, medical apps were catalogued using a Google-based methodology. Keywords of the first 14 categories of the International Classification of Diseases, Tenth Revision, were selected, and in December of each year, searches of the URLs of Google Play and the App Store were conducted using Google Advanced Search. The first 10 results were taken, and apps meeting the inclusion criteria were selected and rated with the iSYScoretedly over 5 years. Differences were found with the international market in terms of apps related to mental health, heart and circulatory system, and cancer, and coincidences were found in the relevance of apps for diabetes control.
Considering morbidity, mortality, and annual treatment costs, the dramatic rise in the incidence of sepsis and septic shock among intensive care unit (ICU) admissions in US hospitals is an increasing concern. Recent changes in the sepsis definition (sepsis-3), based on the quick Sequential Organ Failure Assessment (qSOFA), have motivated the international medical informatics research community to investigate score recalculation and information retrieval, and to study the intersection between sepsis-3 and the previous definition (sepsis-2) based on systemic inflammatory response syndrome (SIRS) parameters.

The objective of this study was three-fold. First, we aimed to unpack the most prevalent criterion for sepsis (for both sepsis-3 and sepsis-2 predictors). Second, we intended to determine the most prevalent sepsis scenario in the ICU among 4 possible scenarios for qSOFA and 11 possible scenarios for SIRS. Third, we investigated the multicollinearity or dichotomy among qSOFA and SIRS predictors.

This obs significant inferences for sepsis treatment initiatives in the ICU and informing hospital resource allocation. These data-driven results further offer design implications for multiparameter intelligent sepsis prediction in the ICU.
Quantifying the prevalence of the qSOFA criteria of sepsis-3 in comparison with the SIRS criteria of sepsis-2, and understanding the underlying dichotomy among these parameters provides significant inferences for sepsis treatment initiatives in the ICU and informing hospital resource allocation. These data-driven results further offer design implications for multiparameter intelligent sepsis prediction in the ICU.
Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.

This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).

Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.

After screening 11,310 articcare data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.

RR2-10.2196/14019.
RR2-10.2196/14019.
In the United States, the most common sexually transmitted infection, human papillomavirus (HPV), causes genital warts and is associated with an estimated 33,700 newly diagnosed cancer cases annually. HPV vaccination, especially for preteens aged 11 to 12 years, is effective in preventing the acquisition of HPV and HPV-associated cancers. However, as of 2018, completion of the 2- or 3-dose HPV vaccination series increased only from 48.6% to 51.1% in teens aged 13 to 17 years, and this increase was observed only in boys. By comparison, 88.7% of teens had more than one dose of the recommended vaccine against tetanus, diphtheria, and acellular pertussis (Tdap), and 85.1% of teens had more than one dose of meningococcal vaccine. Immunizations for Tdap, meningococcal disease, and HPV can occur at the same clinical visit but often do not.

Vaccination against HPV is recommended for routine use in those aged 11 to 12 years in the United States, yet it is underutilized. We aimed to develop an educational video gamt significant.

Video games help preteens in the decision to pursue HPV vaccination. A serious video game on HPV vaccination is acceptable to parents and preteens and can be played as intended. Gamification is effective in increasing preteen interest in HPV vaccination, as game features support decision making for HPV vaccination.

ClinicalTrials.gov NCT04627298; https//www.clinicaltrials.gov/ct2/show/NCT04627298.
ClinicalTrials.gov NCT04627298; https//www.clinicaltrials.gov/ct2/show/NCT04627298.
Negation and speculation are critical elements in natural language processing (NLP)-related tasks, such as information extraction, as these phenomena change the truth value of a proposition. In the clinical narrative that is informal, these linguistic facts are used extensively with the objective of indicating hypotheses, impressions, or negative findings. Previous state-of-the-art approaches addressed negation and speculation detection tasks using rule-based methods, but in the last few years, models based on machine learning and deep learning exploiting morphological, syntactic, and semantic features represented as spare and dense vectors have emerged. However, although such methods of named entity recognition (NER) employ a broad set of features, they are limited to existing pretrained models for a specific domain or language.

As a fundamental subsystem of any information extraction pipeline, a system for cross-lingual and domain-independent negation and speculation detection was introduced with speciaderably better than the previous rule-based and conventional machine learning-based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.
These results show that these architectures perform considerably better than the previous rule-based and conventional machine learning-based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.
The benefits of breastfeeding for both infants and mothers have been well recognized. However, the exclusive breastfeeding rate in China is low and decreasing. Mobile technologies have rapidly developed; communication apps such as WeChat (one of the largest social networking platforms in China) are widely used and have the potential to conveniently improve health behaviors.

This study aimed to assess the effectiveness of using WeChat to improve breastfeeding practices.

This 2-arm randomized controlled trial was conducted among pregnant women from May 2019 to April 2020 in Huzhu County, Qinghai Province, China. Pregnant women were eligible to participate if they were aged 18 years or older, were 11 to 37 weeks pregnant with a singleton fetus, had no known illness that could limit breastfeeding after childbirth, used WeChat through their smartphone, and had access to the internet. A total of 344 pregnant women were recruited at baseline, with 170 in the intervention group and 174 in the control group. Womtum (intervention 111/152, 73.0%; control 96/152, 63.2%) and 4-5 months postpartum(intervention 50/108, 46.3%; control 46/109, 42.2%).

This study is the first effort to promote exclusive breastfeeding through WeChat in China, which proved to be an effective method of promoting exclusive breastfeeding in early life. WeChat health education can be used in addition to local breastfeeding promotion programs.

Chinese Clinical Trial Registry ChiCTR1800017364; http//www.chictr.org.cn/showproj.aspx?proj=29325.

RR2-10.1186/s12889-019-7676-2.
RR2-10.1186/s12889-019-7676-2.Coronavirus disease 19 (COVID-19), caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in China and has spread worldwide with a significant rate of infection. Considering the elevated levels of proinflammatory cytokines in COVID-19, it is suggested that cytokine storms play a critical role in its pathogenesis, including acute respiratory distress syndrome (ARDS). However, there is no specific drug for preventing the cytokine release syndrome (CRS) caused by COVID-19. Indeed, interleukin 6 (IL-6) has been highlighted for its many biological functions, such as immune regulation, inflammatory response, and metabolism. Therapeutic blockade of the IL-6 signaling pathway is expected to reduce the excessive immune reponse observed in COVID-19. Currently, the IL-6 receptor antagonists tocilizumab and sarilumab, have been adopted for preventing CRS during the progression of COVID-19, and remarkable beneficial effects were observed by using these humanized monoclonal antibodies.
Website: https://www.selleckchem.com/peptide/angiotensin-ii-human-acetate.html