METHOD: A generalized linear model (GLM) estimates the relationships between different travel mode indicators (e.g., length of motorway per inhabitants, number of motorcycles per inhabitant, percentage of daily trips on foot and by bicycle, percentage of daily trips by public transport) and the number of passenger transport fatalities. Because this city-level model is developed using data sets from different cities all over the world, the impacts of gross domestic product (GDP) are also included in the model.
CONCLUSIONS: Overall, the results imply that the percentage of daily trips by public transport, the percentage of daily trips on foot and by bicycle, and the GDP per inhabitant have negative relationships with the number of passenger transport fatalities, whereas motorway length and the number of motorcycles have positive relationships with the number of passenger transport fatalities.
OBJECTIVE: To conduct an umbrella review to determine whether there is an association between CVDs and the prevalence of AP in adults.
METHODS: The protocol of the review was registered in the PROSPERO database (CRD42020185753). The literature search was conducted using the following electronic databases: Clarivate Analytics' Web of Science Scopus, PubMed and Cochrane Database of Systematic Reviews, from inception to May, 2020, with no language restrictions. Systematic reviews with or without meta-analysis that evaluated the association between CVDs and AP were included. Other types of studies, including narrative reviews, were excluded. Two reviewers independently performed a literature search, data extraction and quality assessment of included studies. Any disagreements or doubts were resolved by a third reviewer. The quality of the reviews was assessed using the AMSTAR 2 tool (A measurement tool to assess systematic reviews), with 16 items. A final categorization of the systematic reviews classified each as of 'high', 'moderate', 'low' or 'critically low' quality.
RESULTS: Four systematic reviews were included in the current review. Three reviews were graded by AMSTAR 2 as 'moderate' quality, whereas one review was graded as 'critically low' quality.
DISCUSSION: Only one systematic review included a meta-analysis. Substantial heterogeneity amongst the primary studies included within each systematic review was notable in preventing a pooled analysis.
CONCLUSIONS: From the limited 'moderate' to 'critically low' quality evidence available, the current umbrella review concluded that a weak association exists between CVDs and AP. In the future, well-designed, longitudinal clinical studies with long-term follow-up are required.
AIM: To conduct an umbrella review to determine whether there is an association between diabetes and the outcome of root canal treatment.
DATA SOURCE: The protocol of the review was developed and registered in the PROSPERO database (CRD42019141684). Four electronic databases (PubMed, EBSCHOhost, Cochrane and Scopus databases) were used to perform a literature search until July 2019.
STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS: Systematic reviews with or without meta-analyses published in English assessing any outcomes of root canal treatment comparing diabetic and nondiabetic patients were included. Two reviewers were involved independently in study selection, data extraction and appraising the reviews that were included. Disagreements were resolved with the help of a third reviewer.
STUDY APPRAISAL AND SYNTHESIS METHODS: The quality of the reviews was assessed using the AMSTAR tool (A measurement tool to assess systematic reviews), with 11 items. Each AMSTAR item was given a score of 1 if the criterion was met, or 0 if the criterion was not met or the information was unclear.
RESULTS: Four systematic reviews were included. The AMSTAR score for the reviews ranged from 5 to 7, out of a maximum score of 11, and all the systematic reviews were classified as 'medium' quality.
LIMITATIONS: Only two systematic reviews included a meta-analysis. Only systematic reviews published in English were included.
CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Diabetes mellitus is associated with the outcome of root canal treatment and can be considered as a preoperative prognostic factor.
METHODS: This study used the MIT-BIH Normal Sinus Rhythm (nsrdb) and MIT-BIH Atrial Fibrillation (afdb) databases for healthy human (NSR) and atrial fibrillation patient (N and AF) ECG signals, respectively. The extraction of features was based on the dynamic system concept to determine the ω of the ECG signals. There were 35,031 samples used for classification.
RESULTS: There were significant differences between the N & NSR, N & AF, and NSR & AF groups as determined by the statistical t-test (p<0.0001). There was a linear separation at 0.4s(-1) for ω of both databases upon using the thresholding method. The feature ω for afdb and nsrdb falls within the high frequency (HF) and above the HF band, respectively. The feature classification between the nsrdb and afdb ECG signals was 96.53% accurate.
CONCLUSIONS: This study found that features of the ω of atrial fibrillation patients and healthy humans were associated with the frequency analysis of the ANS during parasympathetic activity. The feature ω is significant for different databases, and the classification between afdb and nsrdb was determined.
METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.
RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.
CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
METHODS: Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
RESULTS: Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
CONCLUSION: The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.