Notes

Computational Options for Predicting Older microRNAs.
Coronary heart disease (CHD) is a leading cause of disability and deaths worldwide. Secondary prevention, including cardiac rehabilitation (CR), is crucial to improve risk factors and to reduce disease burden and disability. Accessibility barriers contribute to underutilization of traditional center-based CR programs; therefore, alternative delivery models, including cardiac telerehabilitation (ie, delivery via mobile, smartphone, and/or web-based apps), have been tested. Experimental studies have shown cardiac telerehabilitation to be effective and cost-effective, but there is inadequate evidence about how to translate this research into routine clinical practice.

This systematic review aimed to synthesize research evaluating the effectiveness of implementing cardiac telerehabilitation interventions at scale in routine clinical practice, including factors underlying successful implementation processes, and experimental research evaluating implementation-related outcomes.

MEDLINE, Embase, PsycINFO, and on CR accessibility and participation as well as person-centered, health, and economic outcomes.

International Prospective Register of Systematic Reviews (PROSPERO) CRD42019124254; https//www.crd.york.ac.uk/prospero/display_record.php?RecordID=124254.
International Prospective Register of Systematic Reviews (PROSPERO) CRD42019124254; https//www.crd.york.ac.uk/prospero/display_record.php?RecordID=124254.
Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal.

This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mentmation architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests.

Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.
Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.
Although electronic health records (EHRs) have been widely adopted in health care, effective use of EHR data is often limited because of redundant information in clinical notes introduced by the use of templates and copy-paste during note generation. Thus, it is imperative to develop solutions that can condense information while retaining its value. A step in this direction is measuring the semantic similarity between clinical text snippets. To address this problem, we participated in the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing Consortium (OHNLP) clinical semantic textual similarity (ClinicalSTS) shared task.

This study aims to improve the performance and robustness of semantic textual similarity in the clinical domain by leveraging manually labeled data from related tasks and contextualized embeddings from pretrained transformer-based language models.

The ClinicalSTS data set consists of 1642 pairs of deidentified clinical text snippets annotated in a contin2019 n2c2/OHNLP ClinicalSTS challenge, achieving state-of-the-art results with a Pearson correlation coefficient of 0.9010. This winning system was an ensembled model leveraging the output of IIT-MTL on ClinicalBERT with BioBERT, MT-DNN, and handcrafted medication features.

This study demonstrates that IIT-MTL is an effective way to leverage annotated data from related tasks to improve performance on a target task with a limited data set. This contribution opens new avenues of exploration for optimized data set selection to generate more robust and universal contextual representations of text in the clinical domain.
This study demonstrates that IIT-MTL is an effective way to leverage annotated data from related tasks to improve performance on a target task with a limited data set. This contribution opens new avenues of exploration for optimized data set selection to generate more robust and universal contextual representations of text in the clinical domain.
Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. AZD1656 Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.

This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task.

We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into trabasis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.
Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.
The availability and use of health apps continues to increase, revolutionizing the way mobile health interventions are delivered. Apps are increasingly used to prevent disease, improve well-being, and promote healthy behavior. On a similar rise is the incidence of skin cancers. Much of the underlying risk can be prevented through behavior change and adequate sun protection. Self-monitoring apps have the potential to facilitate prevention by measuring risk (eg, sun intensity) and encouraging protective behavior (eg, seeking shade).

Our aim was to assess health care consumer preferences for sun protection with a self-monitoring app that tracks the duration and intensity of sun exposure and provides feedback on when and how to protect the skin.

We conducted an unlabeled discrete choice experiment with 8 unique choice tasks, in which participants chose among 2 app alternatives, consisting of 5 preidentified 2-level attributes (self-monitoring method, privacy control, data sharing with health care provides, ttribute of self-monitoring method significantly interacted with gender and perceived personal usefulness of health apps, suggesting that female gender and lower perceived usefulness are associated with relatively weaker preferences for automatic self-monitoring.

Based on the preferences of our respondents, we found that the utility of a self-monitoring sun protection app can be increased if the app is simple and adjustable; requires minimal effort, time, or expense; and has an interoperable design and thorough privacy infrastructure. Similar features might be desirable for preventive health apps in other areas, paving the way for future discrete choice experiments. Nonetheless, to fully understand these preference dynamics, further qualitative or mixed method research on mobile self-monitoring-based sun protection and broader preventive mobile self-monitoring is required.

RR2-10.2196/16087.
RR2-10.2196/16087.
The use of social media by hospitals has become widespread in the United States and Western European countries. link2 However, in Japan, the extent to which hospitals and clinics use social media is unknown. Furthermore, recent revisions to the Medical Care Act may subject social media content to regulation.

The purpose of this study was to examine social media use in Japanese hospitals and clinics. We investigated the adoption of social media, analyzed social media content, and compared content with medical advertising guidelines.

We randomly sampled 300 hospitals and 300 clinics from a list of medical institutions that was compiled by the Ministry of Health, Labour and Welfare. We performed web and social media (Facebook and Twitter) searches using the hospital and clinic names to determine whether they had social media accounts. We collected Facebook posts and Twitter tweets and categorized them based on their content (eg, health promotion, participation in academic meetings and publications, public relatimpared to other countries. Social media were mainly used for public relations. Some content disseminated by medical institutions could conflict with medical advertising guidelines. This study may serve as a reference for medical institutions to guide social media usage and may help improve medical website advertising in Japan.
Three-dimensional scans are increasingly used to quantify biological topographical changes and clinical health outcomes. Traditionally, the use of 3D scans has been limited to specialized centers owing to the high cost of the scanning equipment and the necessity for complex analysis software. link3 Technological advances have made cheaper, more accessible methods of data capture and analysis available in the field of dentistry, potentially facilitating a primary care system to quantify disease progression. However, this system has yet to be compared with previous high-precision methods in university hospital settings.

The aim of this study was to compare a dental primary care method of data capture (intraoral scanner) with a precision hospital-based method (laser profilometer) in addition to comparing open source and commercial software available for data analysis.

Longitudinal dental wear data from 30 patients were analyzed using a two-factor factorial experimental design. Bimaxillary intraoral digital scans (TrueDefinition, 3M, UK) and conventional silicone impressions, poured in type-4 dental stone, were made at both baseline and follow-up appointments (mean 36 months, SD 10.
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