Predicting Sleep Quality in Family Caregivers of Dementia Patients From Diverse Populations Using Wearable Sensor Data.

This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness.

Health risks associated with urinary incontinence among older Korean Americans living in subsidized senior housing.

Objectives: We examined the association of urinary incontinence (UI) with physical, mental, and social health among older Korean Americans living in subsidized senior housing.

Design: Data were obtained from surveys conducted in 2023 with older Korean Americans residing in subsidized senior housing in the Los Angeles area ( = 313). UI was measured using a question about the frequency of involuntary urine loss.

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

Purpose: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.

Design: This study was a retrospective, observational study.

Methods: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model.

Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.

Objective: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations.

Methods: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)).

Sleep disturbances and interrelationship between persons with dementia and family caregivers: The lived experiences of Korean American Dyads.

Background: Little research has investigated sleep quality in dyadic interrelationships between persons with dementia (PWD) and family caregivers, particularly among immigrant ethnic minorities, such as Korean Americans.

Purpose: The study aimed to describe lived experiences of sleep disturbances and sleep interrelationships between Korean American PWD and their family caregivers.

Methods: A descriptive qualitative design used semi-structured interviews with cohabitating PWD-caregiver dyads.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

Objectives: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

Methods: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021.

Intestinal Peyer's Patches: Structure, Function, and In Vitro Modeling.

Backgound: Considering the important role of the Peyer's patches (PPs) in gut immune balance, understanding of the detailed mechanisms that control and regulate the antigens in PPs can facilitate the development of immune therapeutic strategies against the gut inflammatory diseases.

Methods: In this review, we summarize the unique structure and function of intestinal PPs and current technologies to establish in vitro intestinal PP system focusing on M cell within the follicle-associated epithelium and IgA B cell models for studying mucosal immune networks. Furthermore, multidisciplinary approaches to establish more physiologically relevant PP model were proposed.

Higher Protein Digestibility of Chicken Thigh than Breast Muscle in an Elderly Digestion Model.

This study investigated the protein digestibility of chicken breast and thigh in an digestion model to determine the better protein sources for the elderly in terms of bioavailability. For this purpose, the biochemical traits of raw muscles and the structural properties of myofibrillar proteins were monitored. The thigh had higher pH, 10% trichloroacetic acid-soluble α-amino groups, and protein carbonyl content than the breast (p<0.

A Data-Driven Approach to Identify the Predictors of Perceived Health Status Among Chinese and Korean Americans.

Asian Americans are the country's fastest-growing racial group, and several studies have focused on the health outcomes of Asian Americans, including perceived health status. Perceived health status provides a summarized view of the health of populations for diverse domains, such as the psychological, social, and behavioral aspects. Given its multifaceted nature, perceived health status should be carefully approached when examining any variables' influence because it results from interactions among many variables.

Evaluation of race/ethnicity-specific survival machine learning models for Hispanic and Black patients with breast cancer.

Objectives: Survival machine learning (ML) has been suggested as a useful approach for forecasting future events, but a growing concern exists that ML models have the potential to cause racial disparities through the data used to train them. This study aims to develop race/ethnicity-specific survival ML models for Hispanic and black women diagnosed with breast cancer to examine whether race/ethnicity-specific ML models outperform the general models trained with all races/ethnicity data.

Methods: We used the data from the US National Cancer Institute's Surveillance, Epidemiology and End Results programme registries.

Effects of CYP2D6*10 allele on the pharmacokinetics of tolperisone.

Tolperisone, a muscle relaxant used for post-stroke spasticity, has been reported to have a very wide interindividual pharmacokinetic variability. It is metabolized mainly by CYP2D6 and, to a lesser extent, by CYP2C19 and CYP1A2. CYP2D6 is a highly polymorphic enzyme, and CYP2D6*wt/*wt, CYP2D6*wt/*10 and CYP2D6*10/*10 genotypes constitute more than 90% of the CYP2D6 genotypes in the Korean population.

Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

Purpose: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).

Design: This study was a retrospective, observational study.

Methods: We extracted and preprocessed the structured electronic health records (EHRs) from a single academic hospital.

Mediator subunit MDT-15/MED15 and Nuclear Receptor HIZR-1/HNF4 cooperate to regulate toxic metal stress responses in Caenorhabditis elegans.

Zinc is essential for cellular functions as it is a catalytic and structural component of many proteins. In contrast, cadmium is not required in biological systems and is toxic. Zinc and cadmium levels are closely monitored and regulated as their excess causes cell stress.

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections.

Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics.

An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing.

Digital rectal examination (DRE) is considered a quality metric for prostate cancer care. However, much of the DRE related rich information is documented as free-text in clinical narratives. Therefore, we aimed to develop a natural language processing (NLP) pipeline for automatic documentation of DRE in clinical notes using a domain-specific dictionary created by clinical experts and an extended version of the same dictionary learned by clinical notes using distributional semantics algorithms.

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

Purpose: The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques.

Subjects And Setting: Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs.

Methods: Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts.

Big data science: A literature review of nursing research exemplars.

Background: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge.

Purpose: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals.

Methods: A literature review of studies published between 2009 and 2015.

Expanding Interprofessional EHR Data in i2b2.

Emerging issues of team-based care, precision medicine, and big data science underscore the need for health information technology (HIT) tools for integrating complex data in consistent ways to achieve the triple aims of improving patient outcomes, patient experience, and cost reductions. The purpose of this study was to demonstrate the feasibility of creating a hierarchical flowsheet ontology in i2b2 using data-derived information models and determine the underlying informatics and technical issues. This study is the first of its kind to use information models that aggregate team-based care across time, disciplines, and settings into 14 information models that were integrated into i2b2 in a hierarchical model.

Modeling Flowsheet Data for Clinical Research.

Health care data included in clinical data repositories (CDRs) are increasingly used for quality reporting, business analytics and research; however, extended clinical data from interprofessional practice are seldom included. With the increasing emphasis on care coordination across settings, CDRs need to include data from all clinicians and be harmonized to understand the impact of their collaborative efforts on patient safety, effectiveness and efficiency. This study characterizes the extended clinical data derived from EHR flowsheet data that is available in the University of Minnesota's CDR and describes a process for creating an ontology that organizes that data so that it is more useful and accessible to researchers.

A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care.

Background: There is wide recognition that, with the rapid implementation of electronic health records (EHRs), large data sets are available for research. However, essential standardized nursing data are seldom integrated into EHRs and clinical data repositories. There are many diverse activities that exist to implement standardized nursing languages in EHRs; however, these activities are not coordinated, resulting in duplicate efforts rather than building a shared learning environment and resources.

Applied nursing informatics research - state-of-the-art methodologies using electronic health record data.

With the pervasive implementation of electronic health records (EHR), new opportunities arise for nursing research through use of EHR data. Increasingly, comparative effectiveness research within and across health systems is conducted to identify the impact of nursing for improving health, health care, and lowering costs of care. Use of EHR data for this type of research requires use of national and internationally recognized nursing terminologies to normalize data.

Determination of tolperisone in human plasma by liquid chromatography/tandem mass spectrometry for clinical application.

We have developed and validated a simple, rapid, and sensitive liquid chromatography analytical method employing tandem mass spectrometry (LC-MS/MS) for the determination of tolperisone, a centrally acting muscle relaxant, in human plasma. After liquid-liquid extraction with methyl t-butyl ether, chromatographic separation of tolperisone was performed using a reversed-phase Luna C(18) column (2.0mm×50mm, 5μm particles) with a mobile phase of 10mM ammonium formate buffer (pH 3.