Notes

Depiction in the total chloroplast genome involving Dendrobium christyanum and it is phylogenetic investigation.
ealth literacy.
Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan's local databases (TLD) to address this issue. However, the international transferability of this model is unclear.

This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model.

The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served a the model.
Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior.

The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application.

We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobimodel successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions.

The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed.

ClinicalTrials.gov NCT03006653; https//clinicaltrials.gov/ct2/show/NCT03006653.
ClinicalTrials.gov NCT03006653; https//clinicaltrials.gov/ct2/show/NCT03006653.
As a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP) techniques, the evaluation protocol of such systems has not been standardized.

The n2c2/OHNLP (National NLP Clinical Challenges/Open Health Natural Language Processing) 2019 FH extraction task aims to encourage the community efforts on a standard evaluation and system development on FH extraction from synthetic clinical narratives.

We organized the first BioCreative/OHNLP FH extraction shared task in 2018. We continued the shared task in 2019 in collaboration with the n2c2 and OHNLP consortium, and organized the 2019 n2c2/OHNLP FH extraction track. The shared task comprises 2 subtasks. Subtask 1 focuses on identifying family member entities and clinical observations (diseases), and subtask 2 expects the association of the living status, side of the family, and cliniconal Encoder Representations from Transformers, convolutional neural network, bidirectional long short-term memory, conditional random field, support vector machine, and rule-based strategies. System performances show that relation extraction from FH is a more challenging task when compared to entity identification task.
A wide variety of methods were used by different teams in both tasks, such as Bidirectional Encoder Representations from Transformers, convolutional neural network, bidirectional long short-term memory, conditional random field, support vector machine, and rule-based strategies. System performances show that relation extraction from FH is a more challenging task when compared to entity identification task.
Alcohol consumption is associated with a wide range of adverse health consequences and a leading cause of preventable deaths. Ride-hailing services such as Uber have been found to prevent alcohol-related motor vehicle fatalities. These services may, however, facilitate alcohol consumption generally and binge drinking in particular.

The goal of the research is to measure the impact of ride-hailing services on the extent and intensity of alcohol consumption. We allow these associations to depend on population density as the use of ride-hailing services varies across markets.

We exploit the phased rollout of the ride-hailing platform Uber using a difference-in-differences approach. We use this variation to measure changes in alcohol consumption among a local population following Uber's entry. Data are drawn from Uber press releases to capture platform entry and the Behavioral Risk Factor Surveillance Systems (BRFSS) Annual Survey to measure alcohol consumption in 113 metropolitan areas. Models are estimatea wide array of adverse health outcomes. Drunk driving rates have fallen for more than a decade, while binge drinking continues to climb. Both trends may be accelerated by ride-hailing services. This suggests that health information messaging should increase emphasis on the direct dangers of alcohol consumption and binge drinking.
Chronic nonspecific low back pain (CNLBP) is one of the most common complex pain conditions, and it is strongly associated with high rates of disability. Even though several studies on Tui na for CNLBP have been reported, to our knowledge there has been no systematic review of the currently available publications.

This study aims to develop a protocol for a systematic review and meta-analysis that will evaluate the effectiveness and safety of Tui na therapy for patients with CNLBP.

An electronic literature search of PubMed, Embase, MEDLINE, Cochrane Library, Springer, Scopus, World Health Organization International Clinical Trials Registry Platform, Physiotherapy Evidence Database (PEDro), Clarivate Analytics, and Chinese biomedical databases (the China National Knowledge Infrastructure, Wan-fang database, Chinese Scientific Journals Database, and Chinese Biomedical Literature Databases) will be conducted. Studies will be screened by two reviewers independently based on titles and abstracts, followed bytients with CNLBP. The proposed review will determine whether Tui na is effective and safe for CNLBP patients.

PROSPERO CRD42020166731; https//www.crd.york.ac.uk/prospero/display_record.php?RecordID=166731.

PRR1-10.2196/20615.
PRR1-10.2196/20615.
Despite potential for benefit, mindfulness remains an emergent area in perinatal mental health care, and evidence of smartphone-based mindfulness training for perinatal depression is especially limited.

The objective of this study was to evaluate the effectiveness of a smartphone-based mindfulness training intervention during pregnancy on perinatal depression and other mental health problems with a randomized controlled design.

Pregnant adult women who were potentially at risk of perinatal depression were recruited from an obstetrics clinic and randomized to a self-guided 8-week smartphone-based mindfulness training during pregnancy group or attention control group. Mental health indicators were surveyed over five time points through the postpartum period by online self-assessment. The assessor who collected the follow-up data was blind to the assignment. Selleck PYR-41 The primary outcome was depression as measured by symptoms, and secondary outcomes were anxiety, stress, affect, sleep, fatigue, memory, and fear.

Ation to postintervention (OR 3.471-27.986). Parity did not show a significant moderating effect; however, for nulliparous women, mindfulness training participants had significantly improved depression symptoms compared to nulliparous attention control group participants (group × time interaction χ

=18.1, P=.001).

Smartphone-based mindfulness training is an effective intervention in improving maternal perinatal depression for those who are potentially at risk of perinatal depression in early pregnancy. Nulliparous women are a promising subgroup who may benefit more from mindfulness training.

Chinese Clinical Trial Registry ChiCTR1900028521; http//www.chictr.org.cn/showproj.aspx?proj=33474.
Chinese Clinical Trial Registry ChiCTR1900028521; http//www.chictr.org.cn/showproj.aspx?proj=33474.
Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings.

The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes.

Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists' registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 fy is needed before the use of machine learning-based decision support systems for insulin initiation can be incorporated into clinical practice.
The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors' treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience.

In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks.

We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based model to embed EHRs and used Bidirectional Encoder Representations from Transformers (BERT) to extract features from free text and concatenate features with structural data as input to our proposed model.
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