Yoxas/statistical_literacy huggingface dataset:
Electrocardiography interpretation is a core clinical skill for all doctors participating in emergency medical services. Given the majority of ECGs initially performed in hospital on admission are reviewed by junior doctors, and the need for life-threatening pathology to be diagnosed or excluded, further understanding of the level of competency in interpretation and factors associated with this are needed. This was a cross-sectional, descriptive, analytical study. The data were collected using a structured questionnaire. This was comprised of two sections; the first section contained questions related to confidence, previous ECG learning and factors thought to be associated with ECG interpretation competence. The second section was an ECG quiz of 10 12-lead ECGs of varying complexity for interpretation assessment. Descriptive and inferential statistics were utilized for data analysis. Sixty-two doctors from foundation year 1 to registrar level working in acute medicine across three hospitals participated. The mean overall percentage score for the ECG quiz was 45%. No association was found between junior doctor training grade and overall score on the ECG assessment. Undergraduate and postgraduate teaching strategies also did not impact competence. Only 9.7% reported themselves as "confident" interpreting ECGs. There was a trend towards higher levels of competency among those who felt they had undergone sufficient ECG teaching and those who sought regular feedback from other clinicians. This study demonstrated low overall levels of ECG interpretation competency among junior doctors in a large acute teaching NHS trust regardless of grade. Factors associated with competency remain unclear.
Electronic cigarettes (ECs) are a recent method to deliver nicotine with less harmful effects than traditional cigarettes. Studying nicotine dependence in adult EC users is a crucial area, but few measures are available to evaluate nicotine dependence induced by EC. Our study aims to estimate the levels of nicotine dependency among adult EC smokers using a modified Fagerström Test of Nicotine Dependence (e-FTND) in Jeddah, Saudi Arabia, and to identify EC-associated sociodemographic and smoking-related factors affecting nicotine dependency. An analytical cross-sectional study was conducted on adults 18 years of age and older in Jeddah, Saudi Arabia, from December 2023 to March 2024. Data were collected from the participants using a pre-tested structured self-administered questionnaire, and nicotine dependence was assessed using the modified e-FTND. Descriptive statistics such as frequency, mean, and standard deviation were applied. Chi-square was used to assess the association between categorical variables. Ordinal regression was used to predict the nicotine dependency levels with different variables. A total of 344 participants were included in the study. The mean e-FTND score for EC users was 4.14 ± 2.45. Females had a lower likelihood of experiencing higher dependence compared to males (OR = 0.52, 95% CI: 0.32, 0.85). Using ECs for more than three years was associated with higher odds of increased dependence (OR = 3.18, 95% CI: 1.28, 7.98; p < 0.001). The use of Pod system devices lowered the odds of developing high nicotine dependence (OR = 0.32, 95% CI: 0.13, 0.75; p = 0.01) compared to Iqos device users, while mechanical Mod device users exhibited a trend towards higher dependence, although it was not statistically significant. Nicotine concentration in ECs had a significant impact on the degree of nicotine dependence. Higher concentrations were associated with increased odds of higher dependence (12-18 mg: OR = 3.26, 95% CI: 1.55, 6.91; >18 mg: OR = 4.53, 95% CI: 2.37, 8.75; p < 0.001). Most exclusive EC users in the study developed a moderate nicotine dependence level. The EC device type and nicotine concentration were significant drivers of nicotine dependence. Additionally, the personal characteristics of the users, such as male gender and duration of use, were associated with a higher risk of dependence. An in-depth understanding of the magnitude of nicotine dependence among EC users will enhance the opportunity for tailored health-enhancing interventions and policies.
Electronic clinical task management systems (ECTMSs) have been developed and adopted by care providers to improve care coordination. Some systems utilised automated reasoning (AR) to enable more intelligent task management functionalities, such as automated task allocation. Yet, the impact of such features on usability remains unclear. Poor usability of health information systems has been described to cause frustration and contribute to patient safety incidents. To design AR features for an ECTMS and to evaluate their impact on usability. In this mixed methods study, four ECTMS feature prototypes were co-designed with two clinicians. For each prototype, one AR variant and one non-AR variant with equivalent functionalities were developed. A moderated usability testing was conducted with seven clinicians to obtain ease-of-use ratings of prototypes and measure task durations. Parameters related to demographics and attitudes of participants were obtained via a questionnaire. A framework analysis was performed to summarise qualitative feedback. To determine statistical relationships of study variables, Spearmańs rank coefficients were calculated and presented as a correlation matrix. Three out of four prototypes received higher median ease-of-use ratings for AR variants and were associated with shorter average task durations. Multiple clinical use cases suitable for AR were identified. Preference for AR was found to moderately correlate with digital proficiency and prior experience with ECTMSs. Insufficient trust in automation, alert fatigue, and system customisation were identified as challenges in the adoption of AR features. This study provides evidence for the potential of AR to enhance usability in ECTMSs. Consideration of psychological and organisational context of users in the feature design was found to be decisive for usability. Future research should explore implications for operational and clinical outcomes.
Electronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper introduces a novel approach that combines deep learning and semantic ontologies to generate hybrid and personalized recommendations by considering "Physical Activity" as a case study. To achieve this, we employ three methods: time-series forecasting, time-series physical activity level classification, and statistical metrics for data processing. Additionally, we utilize a naïve-based probabilistic interval prediction technique with the residual standard deviation used to make point predictions meaningful in the recommendation presentation. The processed results are integrated into activity datasets using an ontology called OntoeCoach, which facilitates semantic representation and reasoning. To generate personalized recommendations in an understandable format, we implement the SPARQL Protocol and RDF Query Language (SPARQL). We evaluate the performance of standard time-series forecasting algorithms [such as 1D Convolutional Neural Network Model (CNN1D), autoregression, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)] and classifiers [including Multilayer Perceptron (MLP), Rocket, MiniRocket, and MiniRocketVoting] using state-of-the-art metrics. We conduct evaluations on both public datasets (e.g., PMData) and private datasets (e.g., MOX2-5 activity). Our CNN1D model achieves the highest prediction accuracy of 97[Formula: see text], while the MLP model outperforms other classifiers with an accuracy of 74[Formula: see text]. Furthermore, we evaluate the performance of our proposed OntoeCoach ontology model by assessing reasoning and query execution time metrics. The results demonstrate that our approach effectively plans and generates recommendations on both datasets. The rule set of OntoeCoach can also be generalized to enhance interpretability.
Electronic components are rapidly updated in the context of expanding application requirements, and communication protocols used in combination with various electronic devices are also emerging. On this basis, IoT technology has developed a variety of sensor devices and gateways, which are widely used in cities. In the field of wisdom, applying IoT technology to classrooms can effectively improve the deficiencies of traditional teaching models. Fuzzy control theory is usually based on fuzzy sets in mathematics, and is combined with neural network, genetic and probability algorithms to form a calculation method. Fuzzy calculation has the ability to simplify the system input of a variety of complex variables, and its applications in the field of education are mainly: provide evaluation of teachers' teaching effectiveness. The advancement of science and technology has promoted the change and updating of the teaching mode. With the continuous advancement of basic education curriculum reform and the continuous deepening of classroom teaching reform, classroom teaching is also in urgent need of reform, from traditional classrooms to smart classrooms. Smart classrooms combine advanced technology with teachers' teaching. Through the dynamic data, the analysis instantly understands the student's learning situation, and then integrates it into education and teaching in a targeted manner. This paper conducts a questionnaire survey on the current situation of smart classroom teaching, and summarizes the current teaching problems. Then, combining the Internet of Things, fuzzy control and deep learning technology, from the two aspects of school teachers and students, it is proposed for smart classroom to promote students' learning effect. With its novel and new-style teaching advantages, smart classroom has gradually entered the public's vision and gained the attention and support of the majority of educators. Taking Grand Wisdom Classroom as an example, it uses the "Internet +" way of thinking and the new generation of information technology such as big data and cloud computing to create intelligent and efficient classrooms, realizing the whole process of application before, during and after class, and promoting the development of students' wisdom. Under the mobile Internet model, students and teachers can communicate anytime and anywhere. Combined with the analysis and application of our big data technology, data-based precision teaching becomes possible. In a real sense, learning before teaching can be realized and teaching can be determined by learning.
Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.
Electronic fetal monitoring is used in most US hospital births but has significant limitations in achieving its intended goal of preventing intrapartum hypoxic-ischemic injury. Novel deep learning techniques can improve complex data processing and pattern recognition in medicine. This study aimed to apply deep learning approaches to develop and validate a model to predict fetal acidemia from electronic fetal monitoring data. The database was created using intrapartum electronic fetal monitoring data from 2006 to 2020 from a large, multisite academic health system. Data were divided into training and testing sets with equal distribution of acidemic cases. Several different deep learning architectures were explored. The primary outcome was umbilical artery acidemia, which was investigated at 4 clinically meaningful thresholds: 7.20, 7.15, 7.10, and 7.05, along with base excess. The receiver operating characteristic curves were generated with the area under the receiver operating characteristic assessed to determine the performance of the models. External validation was performed using a publicly available Czech database of electronic fetal monitoring data. A total of 124,777 electronic fetal monitoring files were available, of which 77,132 had <30% missingness in the last 60 minutes of the electronic fetal monitoring tracing. Of these, 21,041 were matched to a corresponding umbilical cord gas result, of which 10,182 were time-stamped within 30 minutes of the last electronic fetal monitoring reading and composed the final dataset. The prevalence rates of the outcomes in the data were 20.9% with a pH of <7.2, 9.1% with a pH of <7.15, 3.3% with a pH of <7.10, and 1.3% with a pH of <7.05. The best performing model achieved an area under the receiver operating characteristic of 0.85 at a pH threshold of <7.05. When predicting the joint outcome of both pH of <7.05 and base excess of less than -10 meq/L, an area under the receiver operating characteristic of 0.89 was achieved. When predicting both pH of <7.20 and base excess of less than -10 meq/L, an area under the receiver operating characteristic of 0.87 was achieved. At a pH of <7.15 and a positive predictive value of 30%, the model achieved a sensitivity of 90% and a specificity of 48%. The application of deep learning methods to intrapartum electronic fetal monitoring analysis achieves promising performance in predicting fetal acidemia. This technology could help improve the accuracy and consistency of electronic fetal monitoring interpretation.
Electronic health (eHealth) literacy is a relatively new concept used to determine health outcomes. However, it is not well known how eHealth literacy relates to health outcomes such as diabetes self-management. This study was designed to examine the relationships among eHealth literacy, self-efficacy, social support, and self-management in people with Type 2 diabetes. A cross-sectional design was used to examine secondary data from a field survey of people with Type 2 diabetes recruited from outpatient clinics from August to December 2021 ( N = 453). A structural equation model was used that first analyzed the measurement model using confirmatory factor analysis and then tested the hypothesized structural model to estimate the expected relationships among the study variables. The significance of the statistical estimates for the model was assessed based on the 95% bias-corrected bootstrap confidence interval from 5,000 bootstrap resamples. Significant, indirect relationships were found between eHealth literacy and self-management via self-efficacy (β = 0.26, B = 0.17, 95% CI [0.10, 0.24]) and via social support and, in turn, self-efficacy (β = 0.08, B = 0.05, 95% CI [0.04, 0.08]). eHealth literacy, social support, and self-efficacy together explained 58.1% of the variance in self-management. This study provides new evidence regarding how eHealth literacy relates to self-management in people with Type 2 diabetes via two indirect pathways, including self-efficacy alone and social support and self-efficacy in series. An eHealth literacy program for self-management should be developed in clinical practice that includes strategies for inducing synergistic effects from self-efficacy and social support on self-management in people with Type 2 diabetes.
Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.
Electronic health records (EHRs) have provided physicians with user-friendly self-service reporting tools to extract patient data from the EHR. Despite such benefits, physician training on how to use these tools has been limited. At our institution, physicians were faced with prolonged wait time for EHR data extraction requests and were unaware of self-service reporting tool availability in the EHR. Our goal was to develop an EHR data reporting curriculum for physicians and staff and examine the effectiveness of such training. In 2019, physician informaticists developed two interactive sessions to train physicians and staff on self-service reporting tools (Epic® SlicerDicer and Reporting Workbench (RWB)) available in our tertiary children's hospital EHR. We assessed participants' knowledge, confidence, and tool utilization before, after, and 3-months post training via survey. Training sessions occurred between April and August 2021. Thirty-six participants completed the study, with 25 surveys collected immediately post and 22 surveys collected at 3-months post training. Data literacy knowledge pre-test average score improved from 62% to 93% (p < 0.05) immediately post-session and 74% at 3-months post assessment (p = 0.05). Regular tool utilization increased from 29% (RWB) and 34% (SlicerDicer) pre-session to 56% and 44% at 3-months post, respectively. Participants reported increased confidence in performing SlicerDicer model selection, criteria selection, and data visualization as well as RWB report navigation, report creation, report visualization, and describing report's benefits/limitations. Ultimately, physician and staff self-service reporting tools training were effective in increasing data literacy knowledge, tool utilization, and confidence.
Electronic learning (e-learning) has evolved into a popular educational approach since the coronavirus disease 2019 (COVID-19) pandemic. While this represents an additional model for teaching, traditional classroom learning fosters the development of interpersonal skills and enables students to share and discuss specific topics. However, existing research on the comparison of both these modes of learning in the field of dental education is inadequate. This study aimed to evaluate the perceptions of dental students towards both electronic and classroom learning. A cross-sectional questionnaire-based survey was conducted between November 2022 and January 2023 among dental students in Saudi Arabia. Students were questioned on their comparative perceptions of e-learning and classroom learning before, during, and after the COVID-19 pandemic. Questionnaire responses, including demographic data, were collected and tabulated, using electronic data management software. The tabulated data were analyzed to provide descriptive statistics and compare electronic and classroom learning with demographic variables and previous experience with e-learning. Most respondents reported possessing average information technology (IT) skills and prior experience with e-learning. Blackboard Learning Management System (LMS) (Reston, VA: Blackboard Inc.), Zoom (San Jose, CA: Zoom Video Communications Inc.), and Microsoft Teams (Redmond, WA: Microsoft Corporation) were the most commonly used and advantageous e-learning platforms. While the majority of participants found both methods acceptable for problem-based learning sessions and theoretical lectures, they reported e-learning to be less effective than classroom learning for clinical and practical sessions. Regarding e-learning as a preferred method over classroom learning, most responses were "neutral" or "uncertain." Comparing the mean ranks of the ordinal responses for the different teaching methodologies and the nominal responses for e-learning as the preferred method, no statistically significant interactions were observed for demographic characteristics, IT-skill levels, or prior experience with e-learning. Although enhanced performance and learning capacity are enabled through e-learning, the advantages of personal interactions and the feasibility of practical and clinical dental sessions are achieved only through classroom learning.