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210 results for “Medical Data”
Data from: Medical school selection criteria as predictors of medical student empathy: a cross-sectional study of medical students, Ireland
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Data from: Sustainability of professionals’ adherence to clinical practice guidelines in medical care: a systematic review
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Data from: Characteristics and outcomes of women utilizing emergency medical services for third-trimester pregnancy-related complaints in India: a prospective observational study
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Data from: Medical expenditure for chronic diseases in Mexico: the case of selected diagnoses treated by the largest care providers
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Data from: Trial-results reporting and academic medical centers.
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Medical data formatting to improve physician interpretation speed in the military healthcare system
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Data from: Ants medicate to fight disease
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Data from: Gender disparity in physician authorship among commentary articles in high impact medical journals
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Data from: Anticholinergic medications: A potentially modifiable risk factor for development of MCI
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Spending and procedural data on voluntary medical male circumcision
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Optimization data for determination of montelukast and bambuterol as a combined medication
<p>Sensitive, simple and green analytical methodology for simultaneous estimation of bambuterol and montelukast as a combined medication based on their native fluorescence character was developed. The method relies on synchronous spectrofluorimety to solve the problem of the overlapping emission spectra of the studied drugs. Where, utilizing second derivative synchronous spectra enabled the simultaneous quantitation of bambuterol and montelukast without interference. The peak amplitudes of the aqueous solutions at Δλ = 20 nm were estimated at 284 nm & 304 nm for bambuterol and at 374 nm & 384 nm for montelukast. A linear relationship was achieved over the concentration range of 0.2–1.00 μg/mL for bambuterol and 0.4–2.00 μg/mL for montelukast. All factors and parameters were carefully studied to obtain the highest sensitivity and good precision of the proposed method. Additionally, the validation criteria were assessed in accordance with ICH guidelines. The method was utilized for the estimation of both drugs in their raw materials, synthetic mixtures as well their combined tablets with good agreement between its results and those from the comparison method.</p>
Data from: Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records
Objective: Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications. Material and Methods: A retrospective cohort of 76,957 encounters and relevant clinical variables were extracted from a tertiary care, academic hospital electronic medical record (EMR) system between November 2007 and December 2016. Five machine learning methods were used to build prediction models. Prediction tasks from four clinical perspectives with different modeling and evaluation strategies were designed to build and evaluate the models. Results: Experimental analysis of the AKI prediction models built from four different clinical perspectives suggest a realistic prediction performance in cross-validated AUC ranging from 0.720 to 0.764. Discussion: Results show that models built at admission is effective for predicting AKI events in the next day; models built using data with a fixed lead time to AKI onset is still effective in the dynamic clinical application scenario in which each patient's lead time to AKI onset is different. Conclusion: To our best knowledge, this is the first systematic study to explore multiple clinical perspectives in building predictive models for AKI in the general inpatient population to reflect real performance in clinical application.
Data from: Fruit flies medicate offspring after seeing parasites
Hosts have numerous defenses against parasites, of which behavioral immune responses are an important but under-appreciated component. Here we describe a behavioral immune response Drosophila melanogaster utilizes against endoparasitoid wasps. We found that when flies see wasps they switch to laying eggs in alcohol-laden food sources that protect hatched larvae from infection. This oviposition behavior change, mediated by neuropeptide F, is retained long after wasps are removed. Flies respond to diverse female larval endoparasitoids but not to pupal endoparasitoids or males, showing they maintain specific wasp search images. Furthermore, the response evolved multiple times across the genus Drosophila. Our data reveal a behavioral immune response based on anticipatory medication of offspring, and outline a non-associative memory paradigm based on innate parasite recognition by the host.
Data from: Inferring new relations between medical entities using literature curated term co-occurrences
ABSTRACT Objectives Identifying new relations between medical entities, such as drugs, diseases, and side-effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations. Materials and Methods We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect, and a drug treats an indication. To predict these relations and assess their effectiveness, we applied two modeling approaches: multi-task modeling using neural networks, and single-task modeling based on gradient-boosting machines and logistic regression. Results These trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation. Discussion Selecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types. Conclusion The discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.
Data from: Proportion of women presenters at medical grand rounds at major academic centres in Canada: a retrospective observational study
Objective: To assess the proportion of women who presented research or medical grand rounds at five major academic hospitals in Canada. Design: A cross sectional study. Setting: Five major university–affiliated hospitals in Toronto and Calgary. Results: Overall, at all sites and types of academic rounds, there were an average of 17% fewer women presenting than men (p < 0.001). There were an average of 32% and 21% more men presenting at the city wide grand rounds in City A and B respectively (p < 0.001, p = 0.002). There were more male speakers at 4 out of 5 types of rounds. The proportion of women presenting on average was proportional to the Canadian workforce, but on average, below the proportion of female residents and medical students (median ratio 1.1, 0.7 and 0.8 respectively). Conclusion: Our study demonstrated a lower proportion of females in an important outlet for academic recognition and role modeling. This provides a possible contributing factor to the under representation of women in academic medicine, and an area that can be systematically targeted to promote equity.
Data from: Has open data arrived at the British Medical Journal (BMJ)? An observational study
Objective: To quantify data sharing policy compliance at the BMJ by analysing the rate of data sharing practices, and investigate attitudes and examine barriers towards data sharing. Design: Observational study. Setting: The BMJ research archive. Participants: 160 randomly sampled BMJ research articles, excluding meta-analysis and systematic reviews. Main outcome measures: Percentages of research articles that indicated the availability of their raw datasets in their data sharing statements and those that provided their datasets upon request. Results: Fifty out of 160 (31%) research articles indicated the availability of their datasets. Twelve used publicly available data and the remaining 38 were sent email requests to access their datasets. Only 1 publicly available dataset could be accessed and only 6 out of 38 shared their data via e-mail. So only 7/160 research articles shared their datasets, 4.4% (95% confidence interval: 1.8% to 8.8%). Conclusions: Despite the BMJ's strong data sharing policy, sharing rates are low. Possible explanations for low data sharing rates could be: the wording of the BMJ data sharing policy, which leaves room for individual interpretation and possible loopholes; that our email requests ended up in researchers spam folders; and, that researchers are not rewarded in the scientific community for sharing their data. It might be time for a more effective data sharing policy and better incentives for health and medical researchers to share their data.
Data from: Do 'passive' medical titanium surfaces deteriorate in service in the absence of wear?
Globally, more than 1000 tonnes of titanium (Ti) is implanted into patients in the form of biomedical devices on an annual basis. Ti is perceived to be 'biocompatible' owing to the presence of a robust passive oxide film (approx. 4 nm thick) at the metal surface. However, surface deterioration can lead to the release of Ti ions, and particles can arise as the result of wear and/or corrosion processes. This surface deterioration can result in peri-implant inflammation, leading to the premature loss of the implanted device or the requirement for surgical revision. Soft tissues surrounding commercially pure cranial anchorage devices (bone-anchored hearing aid) were investigated using synchrotron X-ray micro-fluorescence spectroscopy and X-ray absorption near edge structure. Here, we present the first experimental evidence that minimal load-bearing Ti implants, which are not subjected to macroscopic wear processes, can release Ti debris into the surrounding soft tissue. As such debris has been shown to be pro-inflammatory, we propose that such distributions of Ti are likely to effect to the service life of the device.
Data from: Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis
Objective: The number of mobile applications addressing health topics is increasing. Whether these apps underwent scientific evaluation is unclear. We comprehensively assessed papers investigating the diagnostic value of available diagnostic health applications using in-built smartphone-sensors. Methods: Systematic Review - Medline, Scopus, Web of Science inclusive Medical Informatics and Business Source Premier (by citation of reference) were searched from inception until December 15th, 2016. Checking of reference lists of review articles and of included articles complemented electronic searches. We included all studies investigating a health application that used in-built sensors of a smartphone for diagnosis of disease. The methodological quality of 11 studies used in an exploratory meta-analysis was assessed with the QUADAS-2 tool and the reporting quality with the STARD statement. Sensitivity and specificity of studies reporting two-by-two tables were calculated and summarized. Results We screened 3'296 references for eligibility. Eleven studies, most of them assessing melanoma screening apps, reported 17 two-by-two tables. Quality assessment revealed high risk of bias in all studies. Included papers studied 1'048 subjects (758 with the target conditions and 290 healthy volunteers). Overall, the summary estimate for sensitivity was 0.82 (95 % confidence interval (CI); 0.56 to 0.94) and 0.89 (95 %CI; 0.70 to 0.97) for specificity. Conclusions The diagnostic evidence of available health apps on Apple's and Google's app stores is scarce. Consumers and healthcare professionals should be aware of this when using or recommending them.
Data from: An integrated iterative annotation technique for easing neural network training in medical image analysis
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.
Data from: Handover training for medical students – a controlled educational trial of a pilot curriculum
Background: Handovers are a critical point of patient care and a significant source of adverse events. The WHO patient safety curriculum provides some structure for handover teaching; in Europe, there is no standardized curriculum for undergraduate handover training. To address this, the Aachen Interdisciplinary Training Centre for Medical Education, developed and established a pilot curriculum for handover training in the context of the EU-funded PATIENT-project Objective: To develop and implement a handover curriculum for medical students and to assess its effect on students' awareness, confidence and knowledge regarding patient safety and handover in multiple settings. Methods: The pilot handover training curriculum was designed following Kern´s principles of curriculum development and was integrated into a curricular course led by departments for anesthesiology and intensive care (AI) at the University Hospital. A controlled educational research study was conducted with 4th year medical students (n=147) who either received the standard existing curriculum (no teaching of handover, n=78) or the pilot handover training (n=69). Paper-based questionnaires regarding attitude, confidence and knowledge towards handover and patient safety were used for pre- and post-assessment. The pilot curriculum consisted of 3 units (1-2 hours each) integrated into a 4-week course of AI. Multiple types of handover (end-of-shift, operating room/post anesthesia recovery unit/ICU, telephone, discharge) were addressed. Results: Students showed a significant increase in knowledge (p<0.01) and self-confidence for the use of standardized handover tools (p<0.01) and accurate handover performance (p<0.01) among the pilot group. Discussion/Conclusion: We developed and implemented a pilot curriculum for undergraduate handover training. Students displayed a significant increase in knowledge and self-confidence for the use of standardized handover tools and accurate handover performance. An evaluation of the curriculum by other faculties is needed. Further studies should evaluate whether the observed effect of a specific handover strategy is associated with a patient benefit.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.