Factors Associated with Evidence-Based Practice Competencies among Taiwanese Nurses: A Cross-Sectional Study.

Evidence-based practice (EBP) is an essential component of healthcare practice that ensures the delivery of high-quality care by integrating the best available evidence. This study aimed to explore factors influencing EBP among nursing professionals in Taiwan. A cross-sectional survey study was conducted with 752 registered nurses and nurse practitioners recruited from a regional teaching hospital in southern Taiwan. EBP competency was evaluated using the Taipei Evidence-Based Practice Questionnaire (TEBPQ). The results showed that participation in evidence-based courses or training within the past year had the strongest association with EBP competencies (Std. B = 0.157, p < 0.001). Holding a graduate degree (Std. B = 0.151, p < 0.001), working in gynecology or pediatrics (Std. B = 0.126, p < 0.001), searching the literature in electronic databases (Std. B = 0.072, p = 0.039), and able to read academic articles in English (Std. B = 0.088, p = 0.005) were significantly associated with higher TEBPQ scores. Younger age (Std. B = -0.105, p = 0.005) and male gender (Std. B = 0.089, p = 0.010) were also identified as factors contributing to higher EBP competencies. The study highlights the importance of ongoing professional development, including EBP training and language proficiency, in enhancing EBP competencies among nursing professionals in Taiwan.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

OBJECTIVE: Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS: Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS: The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS: The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Topical Antibiotic Prophylaxis for Preventing Surgical Site Infections of Clean Wounds: A Systematic Review and Meta-Analysis.

Background: Topical antibiotic agents are not generally indicated for preventing of surgical site infections (SSIs) in clean incisions, and the drug concentrations that should be delivered to local incision sites remain uncertain. The aim of this study was to critically assess the efficacy of topical antibiotic agents in comparison with non-antibiotic agents for preventing SSIs in clean incisions by performing a systematic review and meta-analysis. Methods: We conducted a search of literature in PubMed, Embase, and Cochrane Databases and included randomized controlled trials (RCTs) on topical antibiotic use for patients with clean post-surgical incisions. The primary outcome was the incidence of SSI, presented as the event rate. Eleven RCTs were included. Results: Using random-effects modeling, the pooled risk ratio (RR) of developing a post-surgical incisions infection was 0.83 (95% confidence interval [CI], 0.61-1.16; I2, 0%). In subgroup analyses, no reductions in SSI were observed when topical antibiotic agents were used to treat incisions due to spinal (RR, 0.75; 95% CI, 0.40-1.38; I2, 0%), orthopedic (RR, 0.69; 95% CI, 0.37-1.29; I2, 0%), dermatologic (RR, 0.77; 95% CI, 0.39-1.55; I2, 65%), or cardiothoracic surgeries (RR, 1.31; 95% CI, 0.83-2.06; I2: 0%). The incidence of SSI across different operative phases did not differ for the application of topical antibiotic agents compared with non-antibiotic agents (RR, 0.80; 95% CI, 0.56-1.14; I2, 0%). Conclusions: The results of this meta-analysis show that topical antibiotic agents provide no clinical benefit for preventing SSI in clean incisions.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.

BACKGROUND: Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes. METHODS: We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score. RESULTS: The MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693). CONCLUSIONS: A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients.

BACKGROUND: Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue. METHODS: Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group. RESULTS: The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group. CONCLUSION: The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.

The structure, function, task, and effectiveness of hospital-based child protection teams in Taiwan.

BACKGROUND: Child protection teams (CPTs) are established in many countries with an intent to safeguard children at risk for maltreatment. However, the tasks and effectiveness of CPTs in Taiwan and many countries remain unclear. OBJECTIVE: A two-step, descriptive correlational study aimed to explore the implementation status and needs concerning the structure, functions, tasks, and effectiveness of hospital-based CPTs using a self-developed evaluation tool in Taiwan. PARTICIPANTS AND SETTING: Five experts and 10 CPT members were evaluated the psychometric properties of the evaluation tool. The main study participants comprised 153 CPT members in Taiwan in 2020. METHODS: Content validity, factor analysis, test-retest reliability, and internal consistency were used to evaluate the psychometric properties of the instrument. Descriptive and correlational statistics were to describe the implementation status and needs of the structure, functions, tasks, and effectiveness of hospital-based CPTs and their relationships. RESULTS: The psychometric properties of the tool were acceptable and satisfactory. The mean scores for each dimension of CPT implementation status were 2.77-2.93 (potential range 0-4) with the lowest for collaboration (mean = 1.97) and incentive (mean = 1.93). The average need scores for each dimension ranged 7.96-8.12 (potential range 0-10), indicating high needs for each dimension, particularly in support, cohesion, and incentive. The implementation status was significantly, weakly correlated with the needs. CONCLUSIONS: There is a need to further strengthen the structure and function of the CPTs and to improve its implementation in Taiwan. It is important to improve inter-agency collaboration and to establish an incentive mechanism for hospital CPTs. Working closely with community agencies is also needed to provide a good quality of care to the maltreated child and the family.

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study.

BACKGROUND: Negation and speculation unrelated to abnormal findings can lead to false-positive alarms for automatic radiology report highlighting or flagging by laboratory information systems. OBJECTIVE: This internal validation study evaluated the performance of natural language processing methods (NegEx, NegBio, NegBERT, and transformers). METHODS: We annotated all negative and speculative statements unrelated to abnormal findings in reports. In experiment 1, we fine-tuned several transformer models (ALBERT [A Lite Bidirectional Encoder Representations from Transformers], BERT [Bidirectional Encoder Representations from Transformers], DeBERTa [Decoding-Enhanced BERT With Disentangled Attention], DistilBERT [Distilled version of BERT], ELECTRA [Efficiently Learning an Encoder That Classifies Token Replacements Accurately], ERNIE [Enhanced Representation through Knowledge Integration], RoBERTa [Robustly Optimized BERT Pretraining Approach], SpanBERT, and XLNet) and compared their performance using precision, recall, accuracy, and F1-scores. In experiment 2, we compared the best model from experiment 1 with 3 established negation and speculation-detection algorithms (NegEx, NegBio, and NegBERT). RESULTS: Our study collected 6000 radiology reports from 3 branches of the Chi Mei Hospital, covering multiple imaging modalities and body parts. A total of 15.01% (105,755/704,512) of words and 39.45% (4529/11,480) of important diagnostic keywords occurred in negative or speculative statements unrelated to abnormal findings. In experiment 1, all models achieved an accuracy of >0.98 and F1-score of >0.90 on the test data set. ALBERT exhibited the best performance (accuracy=0.991; F1-score=0.958). In experiment 2, ALBERT outperformed the optimized NegEx, NegBio, and NegBERT methods in terms of overall performance (accuracy=0.996; F1-score=0.991), in the prediction of whether diagnostic keywords occur in speculative statements unrelated to abnormal findings, and in the improvement of the performance of keyword extraction (accuracy=0.996; F1-score=0.997). CONCLUSIONS: The ALBERT deep learning method showed the best performance. Our results represent a significant advancement in the clinical applications of computer-aided notification systems.

Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis.

BACKGROUND: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Therefore, it is important to detect and predict these adverse effects early. Our study aimed to build models using an artificial intelligence/machine learning approach to predict acute hepatitis, acute respiratory failure, and mortality after TB treatment. MATERIALS AND METHODS: Adult patients (age ≥ 20 years) who had a TB diagnosis and received treatment from January 2004 to December 2021 were enrolled in the present study. Thirty-six feature variables were used to develop the predictive models with AI. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. RESULTS: A total of 2248 TB patients in Chi Mei Medical Center were included in the study; 71.7% were males, and the other 28.3% were females. The mean age was 67.7 ± 16.4 years. The results showed that our models using the six AI algorithms all had a high area under the receiver operating characteristic curve (AUC) in predicting acute hepatitis, respiratory failure, and mortality, and the AUCs ranged from 0.920 to 0.766, 0.884 to 0.797, and 0.834 to 0.737, respectively. CONCLUSIONS: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early.

Inferring Bacterial Community Interactions and Functionalities Associated with Osteopenia and Osteoporosis in Taiwanese Postmenopausal Women.

Growing evidence suggests that the gut microbiota and their metabolites are associated with bone homeostasis and fragility. However, this association is limited to microbial taxonomic differences. This study aimed to explore whether gut bacterial community associations, composition, and functions are associated with osteopenia and osteoporosis. We compared the gut bacterial community composition and interactions of healthy postmenopausal women with normal bone density (n = 8) with those of postmenopausal women with osteopenia (n = 18) and osteoporosis (n = 21) through 16S rRNA sequencing coupled with network biology and statistical analyses. The results of this study showed reduced alpha diversity in patients with osteoporosis, followed by that in patients with osteopenia, then in healthy controls. Taxonomic analysis revealed that significantly enriched bacterial genera with higher abundance was observed in patients with osteoporosis and osteopenia than in healthy subjects. Additionally, a co-occurrence network revealed that, compared to healthy controls, bacterial interactions were higher in patients with osteoporosis, followed by those with osteopenia. Further, NetShift analysis showed that a higher number of bacteria drove changes in the microbial community structure of patients with osteoporosis than osteopenia. Correlation analysis revealed that most of these driver bacteria had a significant positive relationship with several significant metabolic pathways. Further, ordination analysis revealed that height and T-score were the primary variables influencing the gut microbial community structure. Taken together, this study evaluated that microbial community interaction is more important than the taxonomic differences in knowing the critical role of gut microbiota in postmenopausal women associated with osteopenia and osteoporosis. Additionally, the significantly enriched bacteria and functional pathways might be potential biomarkers for the prognosis and treatment of postmenopausal women with osteopenia and osteoporosis.

Implementation of a patient-centered mobile shared decision making platform and healthcare workers' evaluation: a case in a medical center.

Shared decision making is a patient-centered clinical decision-making process that allows healthcare workers to share the existing empirical medical outcomes with patients before making critical decisions. This study aims to explore a project in a medical center of developing a mobile SDM in Taiwan. Chi Mei Medical Center developed the mobile SDM platform and conducted a survey of evaluation from healthcare workers. A three-tier platform that based on cloud infrastructure with seven functionalities was developed. The survey revealed that healthcare workers with sufficient SDM knowledge have an antecedent effect on the three perceptive factors of acceptance of mobile SDM. Resistance to change and perceived ease of use show significant effect on behavioral intention. We provided a comprehensive architecture of mobile SDM and observed the implementation in a medical center. The majority of healthcare workers expressed their acceptancem; however, resistance to change still present. It is, therefore, necessary to be eliminated by continuously promoting activities that highlight the advantages of the Mobile SDM platform. In clinical practice, we validated that the mobile SDM provides patients and their families with an easy way to express their concerns to healthcare workers improving significantly their relationship with each other.

An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach.

Background: For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose: Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods: AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results: A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion: We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.

The Prescription of Chinese Herbal Medicine and Risk of Endometriosis in Women with Rheumatoid Arthritis: A Population-Based Cohort Study.

Purpose: The systemic inflammation is believed to provide an outline of the association between rheumatoid arthritis (RA) and endometriosis. This retrospective cohort study aimed to explore the association of Chinese herbal medicine (CHM) use with the prevention of endometriosis onset in women diagnosed with RA. Methods: We utilized the claims data from the National Health Insurance of Taiwan from 2000 to 2009 and excluded individuals diagnosed with endometriosis before being diagnosed with RA, using age at clinical diagnosis. After selection and propensity-score matching, a total of 5992 females aged ≧20 years old and with newly diagnosed RA but without endometriosis at baseline were included, which contained 2996 CHM users and 2996 non-CHM users. All of them were followed until the end of 2013 to measure the incidence of endometriosis. Results: During the study period, we noticed that CHM users had a substantially lower incidence of endometriosis compared to non-CHM users (2.54 vs 5.19 per 1000 person-years). Use of CHM correlated significantly with a lower endometriosis likelihood even after adjusting for potential covariates, with the adjusted hazard ratio of 0.47 (95% confidence interval, 0.35-0.65). A longer duration of CHM use was associated with a reduction in endometriosis risk, especially in those using CHM for more than 730 days. Uses of several herbal products may be associated with a lower risk of endometriosis, like Ge-Gen, Da-Huang, Huang-Qin, Ye-Jiao-Teng, Chuan-Niu-Xi, Shu-Jing-Huo-Xue-Tang, Du-Huo-Ji-Sheng-Tang, Ge-Gen-Tang, Shao-Yao-Gan-Cao-Tang, Ping-Wei-San, Gan-Lu-Yin, and Dang-Gui-Nian-Tong-Tang. Conclusion: Taken together, adding CHM to conventional therapy may reduce the incidence of endometriosis in women with RA. The therapeutic mechanisms and safety of these natural products may be a direction for future clinical studies.

Ascophyllum nodosum and Fucus vesiculosus Extracts Improved Lipid Metabolism and Inflammation in High-Energy Diet-Induced Hyperlipidemia Rats.

Ascophyllum nodosum and Fucus vesiculosus both contain unique polyphenols called phlorotannins. Phlorotannins reportedly possess various pharmacological activities. A previous study reported that the activity of phlorotannin is strongly correlated with the normalization of metabolic function, and phlorotannins are extremely promising nutrients for use in the treatment of metabolic syndrome. To date, no study has explored the antihyperlipidemic effects of phlorotannins from A. nodosum and F. vesiculosus in animal models. Therefore, in the present study, we investigated the effects of phlorotannins using a rat model of high-energy diet (HED)-induced hyperlipidemia. The results showed that the rats that were fed an HED and treated with phlorotannin-rich extract from A. nodosum and F. vesiculosus had significantly lower serum fasting blood sugar (FBS), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total cholesterol (TC), triacylglyceride (TG) and free fatty acids (FFAs) levels and hepatic TG level and had higher serum insulin, high-density lipoprotein cholesterol (HDL-C) levels and lipase activity in their fat tissues than in the case with the rats that were fed the HED alone. A histopathological analysis revealed that phlorotannin-rich extract could significantly reduce the size of adipocytes around the epididymis. In addition, the rats treated with phlorotannin-rich extract had significantly lowered interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) levels and increased superoxide dismutase (SOD) and glutathione peroxidase (GPX) activities than did those in the HED group. These results suggested that the phlorotannin-rich extract stimulated lipid metabolism and may have promoted lipase activity in rats with HED-induced hyperlipidemia. Our results indicated that A. nodosum and F. vesiculosus, marine algae typically used as health foods, have strong antihyperlipidemic effects and may, therefore, be useful for preventing atherosclerosis. These algae may be incorporated into antihyperlipidemia pharmaceuticals and functional foods.

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients.

The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC's AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard's ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.

Hospital-Based Case Management Model for Child Protection Teams in Taiwan: An Action Research Approach.

BACKGROUND: Hospital-based child protection teams play an important role in the multidisciplinary assessment, treatment, care, and rehabilitation of abused children and their families. However, the process by which these teams are built and promoted has not been explored adequately in the literature. PURPOSE: This study was developed to examine the process used to promote child protection team case management and to evaluate the related results. METHODS: An action research model was adopted in this study. The participants and the investigator were members of a child protection team at a medical center in southern Taiwan. Qualitative and quantitative assessments were used to identify problems related to organizational structure, intervention procedures, and evaluation effectiveness. Thereafter, the study program was implemented, and the results were evaluated. Content analysis of the qualitative data, including transcribed interviews with external benchmark members and members of the hospital's team and text entries from the investigator's reflective journal, was conducted. Quantitative data, including monitoring indicators for team case management, were analyzed using descriptive statistics. RESULTS: Three important concepts emerged related to changes in the promotion of case management by hospital child protection teams. These included formulation of a team operation model through visits to benchmark hospitals, establishment of the case management and monitoring mechanism based on team consensus, and expansion of collaboration with external agencies through the establishment of a child and adolescent protection medical regional integration center. The results of the promotion process were affected by factors that included member willingness, teamwork, hospital support, and national policy. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: Use of the hospital child protection team case management model developed in this study was shown to facilitate the provision of consultation services, integrate the opinions and resources of experts from various fields, and allow the timely provision of acute care, follow-up family environment support, and social resources required by children and their family members. These measures help prevent the reoccurrence of child abuse and enable children to grow up healthily and free from violence.

A Computer-Assisted System for Early Mortality Risk Prediction in Patients with Traumatic Brain Injury Using Artificial Intelligence Algorithms in Emergency Room Triage.

Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient's condition to family members.

Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers.

Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.

Screening Tools for Child Abuse Used by Healthcare Providers: A Systematic Review.

BACKGROUND: The early detection of child abuse is essential for children at risk. Healthcare professionals working at hospitals and in the community are often the first to encounter suspected cases of child abuse. Therefore, an accurate identification of child abuse is critical for intervention. However, there is no consensus on the best method to screen for child abuse. PURPOSE: This systematic review was designed to evaluate the relevant psychometric properties and critically appraise the methodological quality of child abuse screening tools used by healthcare providers with children less than 18 years old. METHODS: We searched the Cochrane Library, MEDLINE, Embase, CINAHL, Education Resources Information Center, PubMed, Airiti Library, and OpenGray databases for studies on screening tools used to identify abuse in children published through October 2019 in English or Chinese. Information regarding populations, assessment methods, and accuracy parameters were extracted. Study quality was assessed using the COnsensus-based Standards for the selection of health Measurement INstruments checklist and Grading of Recommendation, Assessment, Development, and Evaluation criteria. RESULTS: Nine hundred thirty-nine abstracts and 23 full-text articles were reviewed for eligibility, and 15 screening tools for child abuse used by healthcare providers were identified. Screening tools often assess the presence of more than one form of abuse, but no single tool covered all forms. Of these, 10 tools screened for a single, discrete type of abuse, including nine physical abuse screening tools (three abusive head trauma tools) and one sexual abuse tool. Eighty percent (n = 12) of the screening tools had a moderate-to-high quality of evidence based on the Grading of Recommendation, Assessment, Development, and Evaluation criteria. However, none of these screening tools achieved an adequate level of evidence based on the COnsensus-based Standards for the selection of health Measurement INstruments checklist. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: In this systematic literature review, 15 assessment tools of child abuse used by healthcare providers were identified, of which nine screened for physical abuse. Screening tools must be valid, succinct, user-friendly, and amenable for use with children at every point of care in the healthcare system. Because of the paucity of informative and practical studies in the literature, findings related to the quality of child abuse screening tools were inconclusive. Therefore, future research should focus on the use of screening tools in the healthcare system to identify effective screening interventions that may help healthcare providers identify child abuse cases as early as possible.

The beneficial effect of synbiotics consumption on Alzheimer's disease mouse model via reducing local and systemic inflammation.

Alzheimer's disease (AD) is a neurodegenerative disease that impairs multiple memory domains without an effective prevention or treatment approach. Amyloid plaque-induced neuroinflammation exacerbates neurodegeneration and cognitive impairment in AD. To reduce neuroinflammation, we applied prebiotics or synbiotics to modulate the gut-brain axis in the AD mouse model. AD-like deficits were reduced in mice treated with synbiotics, suggesting that dietary modulation of the gut-brain axis is a potential approach to delay AD progression.

Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality and contributes to high morbidity worldwide. Patients with COPD have a higher risk for acute respiratory failure, ventilator dependence, and mortality after hospitalization compared with the general population. Accurate and early risk detection will provide more information for early management and better decision making. This study aimed to build prediction models using patients' characteristics, laboratory data, and comorbidities for early detection of acute respiratory failure, ventilator dependence, and mortality in patients with COPD after hospitalization. We retrospectively collected the electronic medical records of 5061 patients with COPD in three hospitals of the Chi Mei Medical Group, Taiwan. After data cleaning, we built three prediction models for acute respiratory failure, ventilator dependence, and mortality using seven machine learning algorithms. Based on the AUC value, the best model for mortality was built by the XGBoost algorithm (AUC = 0.817), the best model for acute respiratory failure was built by random forest algorithm (AUC = 0.804), while the best model for ventilator dependence was built by LightGBM algorithm (AUC = 0.809). A web service application was implemented with the best models and integrated into the existing hospital information system for physician's trials and evaluations. Our machine learning models exhibit excellent predictive quality and can therefore provide physicians with a useful decision-making reference for the adverse prognosis of COPD patients.