PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “computer sciences”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources
PubMed2005

[Study on the validation of the computer science application's activity monitor in assessing the physical activity among adults using doubly labeled water method].

OBJECTIVE: Using doubly labeled water method to validate the colmputer science application's activity monitor (CSA) in assessing physical activity of free-living adults in Beijing, in order to develop equations to predict total daily energy expenditure (TEE) and activity related energy expenditure (AEE) from activity counts (AC) and anthropometric variables. METHODS: A total of 72 healthy adults (33 males and 39 females, mean age 43.6 +/- 4.0 yr) were monitored for 7 consecutive days by CSA. TEE was simultaneously measured using doubly labeled water method. Average AC (counts/min(-1)) was compared with TEE, AEE and physical activity level (PAL). RESULTS: Physical activity determined by AC was significantly related to data on energy expenditures: TEE (r = 0.31, P < 0.01), AEE (r = 0.30, P < 0.05), and PAL (r = 0.26, P < 0.05). Multiple stepwise regression analysis showed that TEE was significantly influenced by gender, fat-free mass (FFM) or BMI and AC (R(2) = 0.52 - 0.70) while AEE was significantly influenced by gender, FFM and AC (R(2) = 0.25 - 0.32). CONCLUSION: AC from CSA activity monitor seemed a useful measure in studying the total amount of physical activity in free-living adults while AC significantly contributed to the explained variation in TEE and AEE.

Activities of Daily Living↗
PubMed2004

How the role of computing is driving new genetics' public policy.

In this paper we will examine some ethical aspects of the role that computers and computing increasingly play in new genetics. Our claim is that there is no new genetics without computer science. Computer science is important for the new genetics on two levels: (1) from a theoretical perspective, and (2) from the point of view of geneticists practice. With respect to (1), the new genetics is fully impregnate with concepts that are basic for computer science. Regarding (2), recent developments in the Human Genome Project (HGP) have shown that computers shape the practices of molecular genetics; an important example is the Shotgun Method's contribution to accelerating the mapping of the human genome. A new challenge to the HGP is provided by the Open Source Philosophy (I computer science), which is another way computer technologies now influence the shaping of public policy debates involving genomics.

Access to Information↗
PubMed1996

Toward a model for nursing informatics.

PURPOSE: To propose a new model for the development of nursing informatics based on historical precedent. SIGNIFICANCE: Nursing informatics is expanding rapidly. The proposed model aids in understanding the areas of research, relating them to each other, and it shows areas where work is missing or should be extended. ORGANIZING FRAMEWORK: Nursing informatics as the interaction of cognitive science, computer science, and information science resting on a base of nursing science. IMPLICATIONS: As this model is tested, it can act as an organizing framework to understand and relate studies of nursing informatics and give organization for future research, education, and development.

Computer Communication Networks↗
PubMed1995

Curriculum of medical informatics and medical technology in the medical faculty.

1. CURRICULUM DESCRIPTION. Twenty years ago, our faculty organized several lessons in a physiology course to inform students about computers. Recently, new courses in informatics were established. In their first year, students take a compulsory course (15 hours=h) of basic computer science (computers databases, networking, and basic non-medical computer software). A special elective course in medical informatics (30h) can be taken in the 4th year (about 20% of students pass tis course). This course includes the following lessons: computers in medicine (2h), scientific information (4h), classification in medicine (2h- including ICD, SNOMED etc.), computer support of clinical decision (2h-calculation principles with demonstration), artificial intelligence (2h), statistical software (2h), hospital information systems (2h), software for practitioners (2h), biosignal and image analysis (4th), computers in pharmacology (2h), computer simulation (2h), support of metabolic care (2h-consultations, risk calculations), and laboratory information systems (2h). The same course, though slightly differences, is used for paramedical students (occupational therapy, health education, and nursing). Medical technology was established in a three year curriculum courses in the 1st year include common courses in electronic devices (60 h), computers and programming (120 h), biophysics (90 h), biomechanics (30 h), and different medical courses (500 h). For the 2nd and 3rd year, 75% of the courses (700 h per year) are technical e.g., medical devices, information systems, signal and picture analysis, laboratory technique, and data protection. 2. CONCLUSION AND PERSPECTIVES. Students of medicine, and some paramedical studies, are able to use computer in their profession after having taken these courses. Bachelors of medical technology find application in biomedical research, hospitals, and medical technology firms.

Curriculum↗
PubMed1994

Information revolution in nursing and health care: educating for tomorrow's challenge.

Current emphasis on the national electronic highway and a national health database for comparative health care reporting demonstrates society's increasing reliance on information technology. The efficient electronic processing and managing of data, information, and knowledge are critical for survival in tomorrow's health care organization. To take a leadership role in this information revolution, informatics nurse specialists must possess competencies that incorporate information science, computer science, and nursing science for successful information system development. In selecting an appropriate informatics educational program or to hire an individual capable of meeting this challenge, nurse administrators must look for the following technical knowledge and skill set: information management principles, system development life cycle, programming languages, file design and access, hardware and network architecture, project management skills, and leadership abilities.

Computer User Training↗
PubMed2004

[Nursing Informatics: state of the art and future of a subspecialty in nursing].

Nursing Informatics is defined as a specialty that combines nursing science, computer science and informatics to capture, process, store and communicate data, information and knowledge from the nursing domain. Therefore, Nursing Informatics is understood as a subspecialty within nursing. In Germany, Switzerland and Austria, Nursing Informatics looks back to more than 10 years of experience in the field. A Nursing Informatics Framework is proposed to represent the scientific and practical work. It goes beyond a mere definition and forms an action space for showing present achievements and the need for future activities. An analysis of the state of the science reveals that applications in patient care, management and education and training are nearly equally covered by Nursing Informatics. However, a deficit in basic research is apparent mainly in formalising and representing nursing knowledge. There is also a lack of system descriptions following the phases of the software-engineering process. In the requirements specification phase for nursing information systems meaningful reporting applications for use in quality assurance, evidence based nursing and management will have to be identified. Systematic software-engineering including scientific evaluations is needed in particular of systems in emerging areas such as case management. However, the activities proposed require a thorough education and training in Nursing Informatics on all levels.

Austria↗
PubMed2005

An algebraic geometry approach to protein structure determination from NMR data.

Our paper describes the first provably-efficient algorithm for determining protein structures de novo, solely from experimental data. We show how the global nature of a certain kind of NMR data provides quantifiable complexity-theoretic benefits, allowing us to classify our algorithm as running in polynomial time. While our algorithm uses NMR data as input, it is the first polynomial-time algorithm to compute high-resolution structures de novo using any experimentally-recorded data, from either NMR spectroscopy or X-Ray crystallography. Improved algorithms for protein structure determination are needed, because currently, the process is expensive and time-consuming. For example, an area of intense research in NMR methodology is automated assignment of nuclear Overhauser effect (NOE) restraints, in which structure determination sits in a tight inner-loop (cycle) of assignment/refinement. These algorithms are very time-consuming, and typically require a large cluster. Thus, algorithms for protein structure determination that are known to run in polynomial time and provide guarantees on solution accuracy are likely to have great impact in the long-term. Methods stemming from a technique called "distance geometry embedding" do come with provable guarantees, but the NP-hardness of these problem formulations implies that in the worst case these techniques cannot run in polynomial time. We are able to avoid the NP-hardness by (a) some mild assumptions about the protein being studied, (b) the use of residual dipolar couplings (RDCs) instead of a dense network of NOEs, and (c) novel algorithms and proofs that exploit the biophysical geometry of (a) and (b), drawing on a variety of computer science, computational geometry, and computational algebra techniques. In our algorithm, RDC data, which gives global restraints on the orientation of internuclear bond vectors, is used in conjunction with very sparse NOE data to obtain a polynomial-time algorithm for protein structure determination. An implementation of our algorithm has been applied to 6 different real biological NMR data sets recorded for 3 proteins. Our algorithm is combinatorially precise, polynomial-time, and uses much less NMR data to produce results that are as good or better than previous approaches in terms of accuracy of the computed structure as well as running time. In practice approaches such as restrained molecular dynamics and simulated annealing, which lack both combinatorial precision and guarantees on running time and solution quality, are commonly used. Our results show that by using a different "slice" of the data, an algorithm that is polynomial time and that has guarantees about solution quality can be obtained. We believe that our techniques can be extended and generalized for other structure-determination problems such as computing side-chain conformations and the structure of nucleic acids from experimental data.

Algorithms↗
PubMed2002

Expectancy and risk for alcoholism: the unfortunate exploitation of a fundamental characteristic of neurobehavioral adaptation.

Psychological investigations of alcohol expectancies over the last 20 years, using primarily verbal techniques, have strongly supported expectancies as an important mediator of biological and environmental antecedent variables that influence risk for alcohol use and abuse. At the same time, rapid developments in neuroscience, cognitive science, affective science, computer science, and genetics proved to be compatible with the concept of expectancy and, in some cases, used this concept directly. By using four principles that bear on the integration of knowledge in the biological and behavioral sciences-consilience, conservation, contingency, and emergence-these developments are merged into an integrated explanation of alcoholism and other addictions. In this framework, expectancy is seen as a functional approach to adaptation and survival that has been manifested in multiple biological systems with different structures and processes. Understood in this context, addiction is not a unique behavioral problem or special pathology distinct from the neurobehavioral substrate that governs all behavior, but is rather a natural (albeit unfortunate) consequence of these same processes. The ultimate intent is to weave a working heuristic that ties together findings from molecular and molar levels of inquiry and thereby might help direct future research. Such integration is critical in the multifaceted study of addictions.

Adaptation, Psychological↗
PubMed2003

Dental informatics: an emerging biomedical informatics discipline.

Biomedical informatics is a maturing discipline. During the last forty years, it has developed into a research discipline of significant scale and scope. One of its subdisciplines, dental informatics, is beginning to emerge as its own entity. While there is a growing cadre of trained dental informaticians, dental faculty and administrators in general are not very familiar with dental informatics as an area of scientific inquiry. Many confuse informatics with information technology (IT), are unaware of its scientific methods and principles, and cannot relate dental informatics to biomedical informatics as a whole. This article delineates informatics from information technology and explains the types of scientific questions that dental and other informaticians typically explore. Scientific investigation in informatics centers primarily on model formulation, system development, system implementation, and the study of effects. Informatics draws its scientific methods mainly from information science, computer science, cognitive science, and telecommunications. Dental informatics shares many types of research questions and methods with its parent discipline, biomedical informatics. However, there are indications that certain research questions in dental informatics require novel solutions that have not yet been developed in other informatics fields.

Dental Research↗
PubMed2005

Comparisons of Physicians' and Nurses' Attitudes towards Computers.

Before starting the implementation of integrated hospital information systems, the physicians' and nurses' attitudes towards computers were measured by means of a questionnaire. The study was conducted in Dubrava University Hospital, Zagreb in Croatia. Out of 194 respondents, 141 were nurses and 53 physicians, randomly selected. They surveyed by an anonymous questionnaire consisting of 8 closed questions about demographic data, computer science education and computer usage, and 30 statements on attitudes towards computers. The statements were adapted to a Likert type scale. Differences in attitudes towards computers between groups were compared using Kruskal-Wallis and Mann Whitney test for post-hoc analysis. The total score presented attitudes toward computers. Physicians' total score was 130 (97-144), while nurses' total score was 123 (88-141). It points that the average answer to all statements was between "agree" and "strongly agree", and these high total scores indicated their positive attitudes. Age, computer science education and computer usage were important factors witch enhances the total score. Younger physicians and nurses with computer science education and with previous computer experience had more positive attitudes towards computers than others. Our results are important for planning and implementation of integrated hospital information systems in Croatia.

Attitude of Health Personnel↗
PubMed2006

Informatics and public health at CDC.

Since CDC acquired its first mainframe computer in 1964, the use of information technology in public health practice has grown steadily and, during the past 2 decades, dramatically. Public health informatics (PHI) arrived on the scene during the 1990s after medical informatics (intersecting information technology, medicine, and health care) and bioinformatics (intersecting mathematics, statistics, computer science, and molecular biology). Similarly, PHI merged the disciplines of information science and computer science to public health practice, research, and learning. Using strategies and standards, practitioners employ PHI tools and training to maximize health impacts at local, state, and national levels. They develop and deploy information technology solutions that provide accurate, timely, and secure information to guide public health action.

Centers for Disease Control and Prevention, U.S.↗
PubMed2006

An adaptive signal compression system with pre-specified reconstruction quality and compression rate.

Two essential properties of a signal compression method are the compression rate and the distance between the original signal and the reconstruction from the compressed signal. These two properties are used to assess the performance and quality of the method. In a recent work [B. Tümer, B. Demiröz, Lecture Notes in Computer Science-Computer and Information Sciences, volume 2869, chapter Signal Compression Using Growing Cell Structures: A Transformational Approach, Springer Verlag, 2003, pp. 952-959], an adaptive signal compression system (ACS) is presented which defines the performance of the system as a function of the system complexity, system sensitivity and data size. For a compression method, it is desirable to formulate the performance of the system as a function of the system complexity and sensitivity to optimize the performance of the system. It would be further desirable to express the reconstruction quality in terms of the same system parameters so as to know up front what compression rate to end up with for a specific reconstruction quality. In this work, we modify ACS such that the modified ACS (MACS) estimates the reconstruction quality for a given system complexity and sensitivity. Once this relation is identified it is possible to optimize either compression rate or reconstruction quality with respect to system sensitivity and system complexity while limiting the other one.

Algorithms↗