Evidence Based Medicine
245 resources related to Evidence Based Medicine
- Topics related to Evidence Based Medicine
- IEEE Organizations related to Evidence Based Medicine
- Conferences related to Evidence Based Medicine
- Periodicals related to Evidence Based Medicine
- Most published Xplore authors for Evidence Based Medicine
The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.
Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.
2013 Sixth International Conference on Developments in eSystems Engineering, 2013
In the current trend towards the evidence based medicine we should not forget previously collected data, neither in hand-written records nor in legacy systems. In this paper we present a process to integrate the data gathered by therapeutic machines of our industrial partner used for treatment of neurological and orthopedic disorders. These devices are deployed in numerous physical therapy clinics ...
2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2015
Clinical Pathway Management Systems have emerged as promising methods and tools in clinical care automation as analogous to workflow management tools in business process management. Nevertheless, they are not fully appropriate yet to model and express the complex and non-deterministic clinical phenomena in which clinicians are interested. In this paper, our overall goal is to contribute to the automation of ...
2008 Conference on Human System Interactions, 2008
The paper presents a computer-based curriculum designed for evidence-based medicine (EBM) training of Romanian undergraduate students. A series of materials and cases were developed and integrated into a virtual training environment in order to provide the students with the opportunity to learn about and to assess their evidence-based medicine knowledge and skills. The interactive Web-based approach was efficient and effective ...
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
Failure to detect and manage heterogeneity between clinical trials included in meta-analysis may lead to misinterpretation of summary effect estimates. This may ultimately compromise the validity of the results of the meta-analysis. Typically, when heterogeneity between trials is detected, researchers use sensitivity or subgroup analysis to manage it. However, both methods fail to explain why heterogeneity existed in the first ...
1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353), 1998
In evidence based medicine a stroke subtype is diagnosed after a sequential search for single etiology. Degree of severity, interaction, and concomitant variables are not considered. Yet, thrombus formation, and possibly vascular rupture involves an interactive process of the vascular wall, flow properties and blood constituents in homeostasis and pathology. Evidence based medicine ignores this process and instead studies stroke ...
Larson Collection interview with Rudolph Peierls
Engineering in Medicine and Biology: Segment 1
EDOC 2010 - Prof. Dr. David Harel Presentation
EMBC 2011 -Keynote (Women in Engineering Program) Re-engineering the War on Cancer: A Call to Action for Personalized Medicine -Mara G. Aspinall
EMBC '09 - Advances in Neuro-rehabilitation
POC AAT-based Diagnostic Technology - Paul Yager - IEEE EMBS at NIH, 2019
How Facial Analysis Technology Can Help Children with Genetic Disorders - IEEE Region 4 Technical Presentation
3D Body-Mapping for Severely Burned Patients - Julia Loegering - IEEE EMBS at NIH, 2019
Surgical Robotics: Medical robotics and computer-integrated interventional medicine
Information Technology: Careers for the information age
The Use of Robotic and Advanced Technology in Neurorehabilitation
Robotics History: Narratives and Networks Oral Histories: Paolo Dario
The Moral Importance of Cybersecurity | IEEE TechEthics Virtual Panel
Designing for Sustainability - GHTC 2012 Session - Christopher Freitas
EMBC 2011-Speaker Highlights-Ron Newbower, PhD
Wireless Framework Development for Personalized Rehabilitation - Angad Jasuja - IEEE EMBS at NIH, 2019
EMBC 2011-Program-Systems in Synthetic Biology (Part I)-Pamela A. Silver
EMBC 2011-Workshop-Nanobiomaterials-Ali Khademhosseini
EMBC 2011-Workshop-Nanobiomaterials-Ehsan Jabbarzadeh
In the current trend towards the evidence based medicine we should not forget previously collected data, neither in hand-written records nor in legacy systems. In this paper we present a process to integrate the data gathered by therapeutic machines of our industrial partner used for treatment of neurological and orthopedic disorders. These devices are deployed in numerous physical therapy clinics worldwide. During a therapy session they collect and store precise measurements of various physical parameters. As they have been used for many years, they hold a large and valuable set of data. One goal of the project was to develop a mechanism for transferring those dispersed data to a central server where they can be analyzed by a team of specialists in medical statistics. Another goal was to develop a software that permits to define flexibly various statistics by those specialists, without a necessity of having IT skills. In addition to the system permitting to collect and analyze the therapy session data, an application has been developed to implement a standardized quality of life questionnaire (EuroQol-5D) that allows to compare the results of the therapy using this machine with other therapy methods. As an incentive to cooperate in the program, the patient receives a personal report about his/her therapy progress, possibly benchmarked against the progress of other patients suffering from the same disease. This report also can be flexibly configured by a software developed in this project. In this way, valuable data, previously dormant on isolated systems worldwide, can be reused to validate and improve the therapy for the benefit of the patients.
Clinical Pathway Management Systems have emerged as promising methods and tools in clinical care automation as analogous to workflow management tools in business process management. Nevertheless, they are not fully appropriate yet to model and express the complex and non-deterministic clinical phenomena in which clinicians are interested. In this paper, our overall goal is to contribute to the automation of clinical pathways with the use data provenance methods and tools. In contrast to commonly developed methods for clinical pathways, we claim that the specification and execution of pathways should include not only a description of structural aspects, but also a description of what a clinician needs to know about the execution when the outcome is produced. Consequently, this requires clinicians to communicate their knowledge, ideas and requirements on data provenance at the modeling phase or execution of a clinical pathway. With this recognition of clinician participation in development, we will develop a new conceptual modeling process for clinical pathways in which clinicians can express their data provenance expectations.
The paper presents a computer-based curriculum designed for evidence-based medicine (EBM) training of Romanian undergraduate students. A series of materials and cases were developed and integrated into a virtual training environment in order to provide the students with the opportunity to learn about and to assess their evidence-based medicine knowledge and skills. The interactive Web-based approach was efficient and effective in the EBM training of undergraduate students, thus suggesting that it could be the appropriate method for teaching evidence-based medicine.
Failure to detect and manage heterogeneity between clinical trials included in meta-analysis may lead to misinterpretation of summary effect estimates. This may ultimately compromise the validity of the results of the meta-analysis. Typically, when heterogeneity between trials is detected, researchers use sensitivity or subgroup analysis to manage it. However, both methods fail to explain why heterogeneity existed in the first place. Here we propose a novel methodology that relies on Rough Set Theory (RST) to detect, explain, and manage the sources of heterogeneity applicable to meta-analysis performed on individual patient data (IPD). The method exploits the RST relations of discernibility and indiscernibility to create homogeneous groups of patients. We applied our methodology on a dataset of 1,111 patients enrolled in 9 randomized controlled trials studying the effect of two transplantation procedures in the management of hematologic malignancies. Our method was able to create three subgroups of patients with remarkably low statistical heterogeneity values (16.8%, 0% and 0% respectively). The proposed methodology has the potential to automatize and standardize the process of detecting and managing heterogeneity in IPD meta-analysis. Future work involves investigating the applications of the proposed methodology in analyzing treatment effects in patients belonging to different risk groups, which will ultimately assist in personalized healthcare decision making.
In evidence based medicine a stroke subtype is diagnosed after a sequential search for single etiology. Degree of severity, interaction, and concomitant variables are not considered. Yet, thrombus formation, and possibly vascular rupture involves an interactive process of the vascular wall, flow properties and blood constituents in homeostasis and pathology. Evidence based medicine ignores this process and instead studies stroke using crisp "all or none" classification where subtypes are distinct and interactively relate only to outcome. The statistical approach of evidence based medicine is founded on probability theory, which, in turn, is rooted in classical set theory where elementhood is all (1) or none (0), and opposites interact only to form the null set. Fuzzy set theory, where set membership is to degree [0, 1], encompasses classical set theory, allows for an interactive process between variables, and becomes the measure of complexity. Fuzzy set theory changes the scientific method of evidence based-medicine. It is a challenge to introduce a new conception of stroke diagnosis and its potential effect on evidence based medicine. This paper shows how complexity based medicine is beginning to replace evidence based medicine by the introduction of fuzzy sets and measures to replace the conventional probability theory based a statistical approach.
Healthcare Service Systems may use and support evidence-based medicine, and to accomplish that, their design has to be patient-centered focusing on quality of evidence and strength of recommendation. Hence, this paper intends to promote the debate on the design of Healthcare Service Systems. We propose a framework that attempts to systematize the design and development of Healthcare Service Systems, especially in terms of software engineering. The framework is intended to support the design of Healthcare Service Systems by understanding a complex health treatment as a project, which would provide naturally patient-centeredness, and by considering Evidence-Based Medicine guidelines and recommendations as architecturally significant requirements in the design of Healthcare Service Systems, which would provide focus on quality of evidence and strength of recommendation. Therefore, we would have a SOA user-centric approach and a systematic service design.
Background: Medical science in the form of ¿evidence based medicine¿ demands when measuring any object that object be isolated from others, from context, from the method of measurement and from the observer. Linear non changing relations amongst variables are thus defined which allow prediction based on probabilities and define information from the resulting statistic. Method and Result: It was hypothesized that information of a non statistical nature could be sought in the individual patient. Fuzzy logic and the geometry of fuzzy theory were considered as viable methods for discovery of the process of disease and response to treatment. The result is that variables of interest are allowed to intermingle with others, their context, and the dynamic of these interactions is measured in such a way that the measurer and the measured coexist in each instant. Conclusion: Isolation as a principle of scientific measurement need not be a requirement for attainment of medical knowledge.
Dynamic evidence-based medicine (DEBM) is defined as the process of finding evidence about the care of individual patients automatically and dynamically in those cases when we cannot rely on any literature or guidelines. In this paper, we develop a framework for DEBM using data mining technologies that make it possible to automatically analyze huge clinical databases and to discover patterns behind them. We define the requirements of a data mining system for DEBM. The following functions are required of the system: (1) support for clinical decision making, and (2) discovery of rare patterns which human beings can hardly find. In order to support clinical decision making, rule discovery methods such as association rule mining are applied to this framework. We adopt a post-analysis approach using a rule base and queries. The discovered rules are collected into a rule base for further analysis. By submitting queries to the rule base, users can obtain keys to evidence for making decisions about clinical care. We preliminarily implement a prototype of a rule base and a post-analysis tool based on our framework. This tool can assist users in analyzing the discovered rules.
Clinical evidence exists in modalities other than published clinical literature, such as: clinical data ranging from patient clinical profiles to clinical trials; clinical experiences of eminent medical practitioners; and medical knowledge bases encapsulating knowledge about patient care, healthcare guidelines and protocols, clinical workflow, and so on. We propose a technology-enriched strategy to exploit advanced computer technologies- knowledge management, data mining, case-based reasoning strategies and Internet technology-within traditional evidence-based medicine systems to derive all-encompassing clinical evidence derived from heterogeneous clinical evidence modalities. The paper features a conceptual overview of an integrated clinical evidence system designed to augment the typical literature-based clinical evidence with additional technology-mediated clinical evidence.
Evidence based medicine (EBM) is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Each year, a significant number of research studies (potentially serving as evidence) are reported in the literature at an ever-increasing rate outpacing the translation of research findings into practice. Coupled with the proliferation of electronic health records, and consumer health information, researchers and practitioners are challenged to leverage the full potential of EBM. In this paper we present a research agenda for leveraging business intelligence and big data analytics in evidence based medicine, and illustrate how analytics can be used to support EBM.
No standards are currently tagged "Evidence Based Medicine"