Applying machine learning to construct an association model for lung cancer and environmental hormone high-risk factors and nursing assessment reconstruction.

INTRODUCTION: To utilize machine learning techniques to develop an association model linking lung cancer and environmental hormones to enhance the understanding of potential lung cancer risk factors and refine current nursing assessments for lung cancer. DESIGN: This study is exploratory in nature. In Stage 1, data were sourced from a biological database, and machine learning methods, including logistic regression and neural-like networks, were employed to construct an association model. Results indicate significant associations between lung cancer and blood cadmium, urine cadmium, urine cadmium/creatinine, and di(2-ethylhexyl) phthalate. In Stage 2, 128 lung adenocarcinoma patients were recruited through convenience sampling, and the model was validated using a questionnaire assessing daily living habits and exposure to environmental hormones. RESULTS: Analysis reveals correlations between the living habits of patients with lung adenocarcinoma and exposure to blood cadmium, urine cadmium, urine cadmium/creatinine, polyaromatic hydrocarbons, diethyl phthalate, and di(2-ethylhexyl) phthalate. CONCLUSIONS: According to the World Health Organization's global statistics, lung cancer claims approximately 1.8 million lives annually, with more than 50% of patients having no history of smoking or non-traditional risk factors. Environmental hormones have garnered significant attention in recent years in pathogen exploration. However, current nursing assessments for lung cancer risk have not incorporated environmental hormone-related factors. This study proposes reconstructing existing lung cancer nursing assessments with a comprehensive evaluation of lung cancer risks. CLINICAL RELEVANCE: The findings underscore the importance of future studies advocating for public screening of environmental hormone toxins to increase the sample size and validate the model externally. The developed association model lays the groundwork for advancing cancer risk nursing assessments.

Application of machine learning and its effects on the development of a nursing guidance mobile app for sarcopenia.

BACKGROUND: Aging leads to changes in the body system, such as sarcopenia. This can result in several health issues, particularly physical and mobility dysfunction. Asian people typically have little awareness of sarcopenia. Thus, this study incorporated nursing instruction into the mobile application design to allow users to easily learn about sarcopenia. OBJECTIVE: This study evaluated a model for predicting high-risk populations for sarcopenia in home settings. We further developed a sarcopenia nursing guidance mobile application and assessed the effectiveness of this application in influencing sarcopenia-related knowledge and self-care awareness among participants. METHODS: Using a one-group pretest-posttest design, data were collected from 120 participants at a teaching hospital in northern Taiwan. This study used an artificial intelligence algorithm to evaluate a model for predicting high-risk populations for sarcopenia. We developed and assessed the sarcopenia nursing guidance mobile application using a questionnaire based on the Mobile Application Rating Scale. RESULTS: The application developed in this study enhanced participants' sarcopenia-related knowledge and awareness regarding self-care. After the three-month intervention, the knowledge and awareness was effectively increase, total score was from 4.15 ± 2.35 to 6.65 ± 0.85 and were significant for all questionnaire items (p values < 0.05). On average, 96.1% of the participants were satisfied with the mobile app. The artificial intelligence algorithm positively evaluated the home-use model for predicting high-risk sarcopenia groups. CONCLUSIONS: The mobile application of the sarcopenia nursing guidance for public use in home settings may help alleviate sarcopenia symptoms and reduce complications by enhancing individuals' self-care awareness and ability. TRIAL REGISTRATION: NCT05363033, registered on 02/05/2022.

The effect of medical material management system app on nursing workload and stress.

AIMS: To develop a clinical medical material management App for nurses, in order to reduce their workload and improve the efficacy of medical material management. DESIGN: The single-group pre- and post-test experimental design was adopted. METHODS: The subjects were nurses in the intensive care units of a regional hospital in Hsinchu City enrolled by purposive sampling. Single-group pre-tests and post-tests were conducted. The research period was from November 2019 to March 2020. The workload, stress, and information acceptance of 57 nurses before and after the intervention of the Medical Equipment App were collected. The research tools included a structured questionnaire, which includes open questions that cover the aspects of workload, stress, and information acceptance intention of nurses, as well as a demographic questionnaire, which collects the basic personal data, including gender, age, years of service, educational level, nursing ability level, use ability of IT products, and unit type. The results were analyzed and compared using SPSS, APP Inventor, and data mining modeling to determine the effects of the App. RESULTS: After employing the Shift Check App, the average workload of nurses was effectively reduced, in particular, the workload reduction of the N1 level nursing ability was greater than that of N2. In addition to satisfaction, the scores of information acceptance intention in all aspects, including behavioral intention, technology use intention, and contributing factors, all increased. CONCLUSION: The use of information technology products to assist medical material management in clinical practice has a significant effect on the load reduction of nurses and improvement of satisfaction. CLINICAL RELEVANCE: The App developed in this study can improve nurses' work satisfaction, quality of care and workload reduction.

[The Mindset and Realization of Precision Care Provided by the Science of Ambient-Assisted Living].

Smart care has become a trend in care institutions and households in recent years. Ambient-assisted living (AAL) has been a topic of increased academic interest over the past decade in line with societal aging and the proliferation of internet and mobile technologies. At the extreme end of AAL is "over-science", a situation in which human functions are over replaced by scientific technologies. This may not only jeopardize the health of older individuals but exacerbate the progress of their dysfunctions by ignoring their desire for self-respect and autonomy. Therefore, the aim of AAL should be to create a web ecosystem rather instead of creating a linearly clustered combination of computerized gadgets.

Effects of mobile health on HIV risk reduction for men who have sex with men.

Mobile health (M-Health) has become a novel method for HIV prevention and the effects need to be promoted. The study purpose was to exam how a smartphone application (app) reduces HIV risky behaviour in men who have sex with men (MSM). The Safe Behaviour and Screening (SBS) app was developed, and included five features: record, output, and resources connection; information provision; testing services; interaction; and online statistics. A random assignment was used. The experimental group used the SBS app for six months. The control group did not use any intervention. There were 130 participants in the experimental group, and 135 in the control group. The average age of all subjects was 27.38 (SD = 5.56). Compared to the control group, the experimental group had significantly higher mean score of safe behaviour knowledge, motivation, and skills; percentage of condom use during anal intercourse; frequency of searching for testing resources and getting HIV and syphilis tests. The frequency of anal intercourse and recreational drug usage were significantly lower in the experimental group. The SBS app could decrease the HIV risky behaviour among MSM and be applied to HIV prevention and nursing intervention.

Exploration and forecasting of behaviours and factors relating to fruit and vegetable intake among seniors in the community.

OBJECTIVE: The present study investigated the current status of fruit and vegetable intake among seniors and assessed the relationship between personal background factors, social psychological factors and environmental factors of the study participants and their fruit and vegetable consumption behaviour. DESIGN: Research data were collected through individual interviews using a questionnaire developed by the authors. SPSS for Windows 15·0 statistical software was used to process and analyse the data. SETTING: Elderly individuals sampled from all twenty-nine administration units of Keelung City's Renai District were interviewed. SUBJECTS: Study participants included 398 residents aged 65 years or older. RESULTS: On average, study participants ate five daily servings of fruits and vegetables on 2·86 d/week. The important variables influencing fruit and vegetable consumption were education level, outcome expectancy, social support, self-efficacy, frequency of dining out and role modelling. Educated participants consumed more fruits and vegetables than those without education. Outcome expectancy, social support, self-efficacy and role modelling had positive impacts on fruit and vegetable intake, but frequency of dining out had a negative impact on fruit and vegetable intake. The significant predictors of fruit and vegetable intake behaviour were education level, outcome expectancy, social support and frequency of dining out. Among those variables, social support was the most influential factor. CONCLUSIONS: Our findings supported the conclusion that health education strategies to increase fruit and vegetable intake among seniors should include the variables of social support and outcome expectancy.

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

Applying Artificial Intelligence Constructing a Prediction Model Related to Environmental Hormones and Breast Cancer Incidence.

Breast cancer is the second leading cause of death in the world and the age of diagnosis is younger and younger. The research is aimed to make a prediction model related to environmental hormones and breast cancer incidence. First, we analyzed lab data to figure out the risk factor of breast cancer. By using Chi-square, Neural network and logistic regression, we find out that Lead, Copper, Zinc, Mercury, Chromium, Chloramphenicol, Sulfonamides, Penicillin and metabolites of phthalates MEP, MBHP related to incidence of breast cancer. These risk factors will be verified by questionnaire of daily habit survey of breast cancer patients. We will establish the relationship between breast cancer and environmental hormones and make public attention to risks of environmental hormones.

Feasibility Study on Detecting Breast Cancer Lymph Node Metastasis and Optimizing Nursing Workflow.

This study explores using AI for detecting lymph node metastasis in breast cancer, aiming to analyze extensive medical imaging data. AI assists clinicians in identifying metastases and formulating precise treatment plans. Employing image analysis and machine learning, the research assesses AI's potential to recognize patterns within medical imaging data. The study evaluates the feasibility of integrating AI-based detection into diagnostic workflows. Collaborative efforts with hospitals include collecting annotated breast cancer lymph node data, optimizing workflows, and clinically validating results. Anticipated outcomes aim to highlight AI's crucial role in enhancing early detection and treatment decisions for breast cancer patients. Anticipated results aim to underscore the crucial role of AI in improving early detection and treatment decision-making for breast cancer patients while optimizing efficiency in the operating room nursing workflow.

Enhancing Citizen Health Literacy Through Digital Technology.

Contemporary society faces significant public health challenges with prevalent unhealthy behaviors and environmental risks leading non-communicable diseases. This study assesses health awareness campaigns'to effectiveness in a specific Taiwanese municipality, focusing on the impact of a health care management system. Objectives include enhancing public health literacy and evaluating community health promotion. The proposed system includes a personalized assistant app, personnel education, and events covering topics like hypertension, diabetes, diet, exercise, and chronic disease prevention. In summary, the research advocates for effective public health interventions, integrating digital technologies, personalized health management, and community engagement to promote health literacy and healthier lifestyles.

Diagnostic Yield and Model Prediction Using Wearable Patch Device in HFpEF.

Heart failure (HF) is a prevalent global health issue projected to escalate, notably in aging populations. The study aimed to identify predictive markers for Heart Failure with preserved Ejection Fraction (HFpEF). We scrutinized vital parameters like age, BMI, eGFR, and comorbidities like atrial fibrillation, coronary artery disease (CAD), diabetes mellites (DM). Evaluating phonocardiogram indicators-third heart sound(S3) and Systolic Dysfunction Index (SDI)-our logistic regression revealed age (≥ 65years), BMI (≥ 25 kg/m2), eGFR (<60 mL/min/1.73m2), CAD, DM, S3 intensity ≥5, and SDI ≥5 as HFpEF predictors, with AUC = 0.816 (p < .001). ROC diagnosis curve showed that the sensitivity, specificity and Youden's index J of the model were 0.755, 0.673 and 0.838, respectively. Nonetheless, further exploration is crucial to delineate the clinical applicability and constraints of these markers.

The development and impact of an app for a smart drug interaction reminder system.

BACKGROUND: Improved access to media and medical knowledge has elicited stronger public health awareness. OBJECTIVE: This study developed a smart drug interaction reminder system for patients to increase knowledge and reduce nurse workload. METHODS: This study used a single-group pre-test/post-test design and applied mining techniques to analyze the weight and probability of interaction among various medicines. Data were collected from 258 participants at a teaching hospital in northern Taiwan using convenience sampling. An app was used to give patients real-time feedback to obtain access to information and remind them of their health issues. In addition to guiding the patients on medications, this app measured the nurses' work satisfaction and patients' knowledge of drug interaction. RESULTS: The results indicate that using information technology products to assist the app's real-time feedback system promoted nurses' work satisfaction, improved their health education skills, and helped patients to better understand drug interactions. CONCLUSION: Using information technology to provide patients with real-time inquiring functions has a significant effect on nurses' load reduction. Thus, smart drug interaction reminder system apps can be considered suitable nursing health education tools and the SDINRS app can be integrated into quantitative structure-activity relationship intelligence in the future.

An Analysis of Waiting Time for Emergency Treatment and Optimal Allocation of Nursing Manpower.

OBJECTIVE: Emergency care is the frontline of the healthcare system. Taiwanese typically seek emergency care when suffering from an acute or unknown illness, which leads to a large number of emergency patients and the related misallocation of nursing manpower, and the excessive workloads of emergency service providers have become serious issues for Taiwan's medical institutions. PARTICIPANTS: This study conducted purposive sampling and recruited patients and nursing staffs from the emergency room of a medical center in New Taipei City as the research participants. METHODS: This study applied the queueing theory and the derived optimal model to solve the problems of excessive workloads for emergency service providers and misallocation of nursing manpower, in an attempt to provide decision makers with more flexible resource allocation and process improvement suggestions. RESULTS: This study analyzed the causes of emergency service overload and identified solutions for improving nursing manpower utilization. CONCLUSIONS: A wait-time model and the queueing theory were used to determine resource parameters for the optimal allocation of patient waiting times and to develop the best model for estimating nursing manpower.

Application of Machine Learning in Developing Decision-Making Support Models for Decompressed Vertebroplasty.

BACKGROUND: The common treatment methods for vertebral compression fractures with osteoporosis are vertebroplasty and kyphoplasty, and the result of the operation may be related to the value of various measurement data during the operation. MATERIAL AND METHOD: This study mainly uses machine learning algorithms, including Bayesian networks, neural networks, and discriminant analysis, to predict the effects of different decompression vertebroplasty methods on preoperative symptoms and changes in vital signs and oxygen saturation in intraoperative measurement data. RESULT: The neural network shows better analysis results, and the area under the curve is >0.7. In general, important determinants of surgery include numbness and immobility of the lower limbs before surgery. CONCLUSION: In the future, this association model can be used to assist in decision making regarding surgical methods. The results show that different surgical methods are related to abnormal vital signs and may affect the length of hospital stay.

Comparison of different predicting models to assist the diagnosis of spinal lesions.

In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients.In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods.Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy.We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care.

Sex Differences between Urinary Phthalate Metabolites and Metabolic Syndrome in Adults: A Cross-Sectional Taiwan Biobank Study.

BACKGROUND: Phthalates are widely used in consumer products, food packaging, and personal care products, so exposure is widespread. Several studies have investigated the association of phthalate exposure with obesity, insulin resistance, and hypertension. However, little is known about the associations of phthalate exposure with sex, age, and menopausal status in metabolic syndrome (MetS). The purpose of this study was to investigate the association between 11 urinary phthalate metabolite concentrations and metabolic syndrome in adults. METHODS: We conducted a cross-sectional analysis of 1337 adults aged 30-70 years from the Taiwan Biobank 2016-2020. Prevalence odds ratios (POR) and 95% confidence intervals (CIs) were calculated using logistic regression and stratified by sex, age, and menopausal status. RESULTS: Participants with MetS comprised 16.38%. Higher concentrations of MEP metabolites were associated with more than two- to three-fold increased odds of MetS in males and males ≥ 50 years (adj. POR Q3 vs. Q1 = 2.13, 95% CI: 1.01, 4.50; p = 0.047 and adj. POR Q2 vs. Q1 = 3.11, 95% CI: 0.13, 8.63; p = 0.029). When assessed by menopausal status, postmenopausal females with higher ∑DEHP concentrations had more than nine-fold higher odds of MetS compared with postmenopausal females with the lowest ∑DEHP concentrations (adj. POR Q3 vs. Q1 = 9.58, 95% CI: 1.18, 77.75; p = 0.034). CONCLUSIONS: The findings suggest differential associations between certain phthalate metabolites and MetS by sex, age, and menopausal status.

Application of Artificial Intelligence in the Establishment of an Association Model between Metabolic Syndrome, TCM Constitution, and the Guidance of Medicated Diet Care.

BACKGROUND: This study conducted exploratory research using artificial intelligence methods. The main purpose of this study is to establish an association model between metabolic syndrome and the TCM (traditional Chinese medicine) constitution using the characteristics of individual physical examination data and to provide guidance for medicated diet care. METHODS: Basic demographic and laboratory data were collected from a regional hospital health examination database in northern Taiwan, and artificial intelligence algorithms, such as logistic regression, Bayesian network, and decision tree, were used to analyze and construct the association model between metabolic syndrome and the TCM constitution. Findings. It was found that the phlegm-dampness constitution (90.6%) accounts for the majority of TCM constitution classifications with a high risk of metabolic syndrome, and high cholesterol, blood glucose, and waist circumference were statistically significantly correlated with the phlegm-dampness constitution. This study also found that the age of patients with metabolic syndrome has been advanced, and shift work is one of the risk indicators. Therefore, based on the association model between metabolic syndrome and TCM constitution, in the future, metabolic syndrome can be predicted through the syndrome differentiation of the TCM constitution, and relevant medicated diet care schemes can be recommended for improvement. CONCLUSION: In order to increase the public's knowledge and methods for mitigating metabolic syndrome, in the future, nursing staff can provide nonprescription medicated diet-related nursing guidance information via the prediction and assessment of the TCM constitution.

Predicting Long-Term Mortality with Biomarkers Level for Homecare Patients.

Electronic health record (EHR) systems have been used widely in research. However, most of the EHRs are highly dimensional and it is challenging to analyze such large data set. Bioinformatics is an interdisciplinary science with a focus on data management and interpretation for complex biological phenomena. We investigated biomarkers of nutrition from 3001 patients. Multivariate-adjusted hazard ratios of mortality were calculated according to both albumin and sodium levels. We explore the association of aging predicted by all-cause mortality in future.

Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.

This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics.

Decompressed percutaneous vertebroplasty: a secured bone cement delivery procedure for vertebral augmentation in osteoporotic compression fractures.

UNLABELLED: The purpose of this study was to assess the efficacy of a new assistive procedure for injecting cement in percutaneous vertebroplasty (PV). Percutaneous vertebroplasty is frequently used for treating patients with osteoporotic vertebral compression fractures. However, the leakage of bone cement during PV may lead to serious complications, such as spinal cord compression or pulmonary embolism. Herein we present a secure procedure designed to safely and effectively deliver the bone cement into the vertebral column. MATERIALS AND METHODS: Thirty-five patients with a total of 50 levels of osteoporotic compression fracture were consecutively recruited for the study. During a routine PV operation, acrylic cement was injected with a simultaneous application of a continuous negative pressure to the contralateral side of the vertebral body. This negative pressure exerts a pulling force that attracts the bone cement to flow within the vertebral body. RESULTS: With the proposed decompressed PV procedure, cross-filling of the vertebrographies was achieved for all 50 fracture levels, with no paravertebral venous plexus leakage. Three of the 50 levels (6%) exhibited contrast-medium leakage into the intradisc or cortical defect regions. After decompressed cement injection, excellent cross-filling of bone cement deposition was achieved in 38 of the 50 levels (76%; cement cross-filling region >75%), good cross-filling deposition was achieved in 7 levels (14%; cement cross-filling region >50%), deposition was poor in 3 levels (6%; cement cross-filling region <50%), and deposition failed in 2 levels (4%; fixed cement with no sign of cross-filling). Routine postoperative reviews revealed that six fracture levels (12%) had minimal cement leakage, with two leaking into the disc and four into paravertebral cortical defect regions. CONCLUSIONS: Compared to the reported 20-88% cement leakage rate for the conventional PV procedure, the proposed decompressed PV procedure offers a more secure and effective way to perform cement injection, and reduces the likelihood of cement leakage.