Biomedical informatics

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The use of receiver operating characteristic curves in biomedical informatics

The use of receiver operating characteristic curves in biomedical informatics

Receiver operating characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models for decision support, diagnosis, and prognosis. ROC analysis investigates the accuracy of a modelÕs ability to separate positive from negative cases (such as predicting the presence or absence of disease), and the results are independent of the prevalence of positive cases in the study population. It is especially useful in evaluating predictive models or other tests that produce output values over a continuous range, since it captures the trade-off between sensitivity and specificity over that range. There are many ways to conduct an ROC analysis. The best approach depends on the experiment; an inappropriate approach can easily lead to incorrect conclusions. In this article, we review the basic concepts of ROC analysis, illustrate their use with sample calculations, make recommendations drawn from the literature, and list readily available software.
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Application of biomedical informatics to chronic pediatric diseases: a systematic review

Application of biomedical informatics to chronic pediatric diseases: a systematic review

In conclusion, our systematic review has revealed several important applications of biomedical informatics in pedi- atric chronic diseases. Published studies suggested posi- tive impacts of informatics predominantly in pediatric asthma. Our review has also highlighted important future directions for research. It is clear, for instance, that rigor- ous studies are needed to gain a better understanding of the role of informatics in inpatient settings, as well as their impact on workflows, organizational structures, health professional and patient roles, costs and equitable access to health services among members of disadvantaged com- munities.
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Biomedical informatics and translational medicine

Biomedical informatics and translational medicine

interventions might be worthy to consider[12]. Next, directed evaluations (e.g., randomized controlled trials) are used to identify the efficacy of the intervention and to provide further insights into why a proposed inter- vention works[12]. Finally, the ultimate success of an intervention is the identification of how it can be appro- priately scaled and applied to an entire population[12]. The various contexts presented across the translational medicine spectrum enable a “ grounding ” of biomedical informatics approaches by providing specific scenarios where knowledge management and integration approaches are needed. Between each of these steps, translational barriers are comprised of the challenges associated with the translation of innovations developed through bench-based experiments to their clinical vali- dation in bedside clinical trials, ultimately leading to their adoption by communities and potentially leading to the establishment of policies. The crossing of each translational barrier ("T1, ” “ T2, ” and “ T3, ” respectively corresponding to translational barriers at the bench-to- bedside, bedside-to-community, and community-to-pol- icy interfaces; as shown in Figure 1) may be greatly enabled through the use of a combination of existing and emerging biomedical informatics approaches[9]. It is particularly important to emphasize that, while the major thrust is in the forward direction, accomplish- ments, and setbacks can be used to valuably inform both sides of each translational barrier (as depicted by the arrows in Figure 1). An important enabling step to
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I. Proposal To Expand the Current Biomedical Informatics Graduate Certificate and Rename It as Informatics Graduate Certificate

I. Proposal To Expand the Current Biomedical Informatics Graduate Certificate and Rename It as Informatics Graduate Certificate

Biomedical Informatics (BMI) has an approved BMI certificate listed with the Graduate School. Nursing Informatics (NI) has a “post-master’s” certificate program internal to the College of Nursing. The NI program would like to have a certificate program through the Graduate School and have the certificate available to any post-baccalaureate student. Moreover, the current BMI certificate is listed as interdisciplinary; however, the program description explicitly states that BMI courses must make up 12 of the 15 required hours, which would exclude students from taking NI courses, and that students must be admitted to the BMI program in the School of Medicine rather than coming through other departments or colleges. Other programs such as the College of Nursing would possibly lose tuition reimbursement revenues and student credits if nursing students enrolled in the BMI Certificate Program versus an NI program. Rather than create a separate certificate for NI, we propose expanding the existing BMI certificate from Biomedical to an interdisciplinary Informatics certificate to incorporate nursing and other informatics students. We propose renaming the program as “Informatics Graduate Certificate.”
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Customized biomedical informatics

Customized biomedical informatics

With the advent of rapid advancement of technology, coupled with decrease in cost of sequencing, it will not be long when every individual will carry their genome-chip which would be comprising of the set of chromosomal sequences, along with informa- tion of SVs and SNPs already determined. Many ventures have already started on this line to tap upon the opportunities that this changing world of medical informatics and genomics has to offer. In fact, this would be a practice which we might want to do early in the life of an individual say within a week after his birth. Let’s say we take it a step further and obtain the DNA sample from the fetus itself, thus being able to do analysis of the baby which is to be born. With the power of prediction and integrating it to powerful relational databases we can tell a-priori as to what are the chances of the baby to be healthy in general. We would be able to predict disease susceptibility of the new born baby as well as characteristic traits a-priori, thereby given an opportunity for the mother to decide whether to have the baby or not, and if so what all things she should be caring about.
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Data fusion techniques for biomedical informatics and clinical decision support

Data fusion techniques for biomedical informatics and clinical decision support

compound figures, and image retrieval using the 2013 ImageCLEF image set [39] (see [40] for an overview of the results of all ImageCLEF biomedical image retrieval tasks 2004- 2013); 4) biomedical image modality classification using image clustering with respect to specified features, expert labeling of the (relatively few) clusters, and image classification based on the cluster labels [41]; 5) modality classification of biomedical literature figures comparing the effectiveness of SVM classification using hand- crafted features versus a deep learning classifier [42]; 6) extracting endoscopic images from the biomedical literature [43] and distinguishing true endoscopic images from confounding images; 7) classification of radiological signs in abdominal CT images [44]; 8) classification of view (frontal or lateral) in chest X-ray images [45]; 9) classification of Visible Human biomedical images into body segment classes ( head and neck, thorax, abdomen, pelvis, and lower limb) by image features [46]; 10) methods to exploit "pointers" (such as arrows) or labels (such as letters or numbers) embedded within biomedical images, for image analysis and retrieval [47]; 11) the use of text associated with biomedical images to enhance image modality classification [48] and retrieval [49, 50]; and 12) modality- based classification over a set of 742 images manually annotated by modality (such as radiological or photo) selected from the 2004- 2005 issues of the British Journal of Oral and Maxillofacial Surgery using global, histogram- based, texture image illustration features, and basis function luminance histogram correlation features computed from the annotated images [1].
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Why Can't It All Be On the Web?: The Information Needs of Biomedical Informatics Scientists

Why Can't It All Be On the Web?: The Information Needs of Biomedical Informatics Scientists

Yarfitz and Ketchell covered the basic issues that all medical libraries must cover in order to target bioinformatics scientists: a web presence, training courses, a needs analysis, co[r]

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On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

For this category, we focus only on visualization tools in the biomedical context where there are many examples for visualization as presentation. As databases have become an integral part of dissemination and mining in biomedicine, the consolidation of such experiments data already brought up comprehensive tools for managing and sharing data. To name one, the Cell Centered Database [38] is a public image repository for managing and sharing (3D) imaging data. Next to image databases there is also a wide variety of different visualization tools, including interaction networks, pathway visualizations, multivariate omics data visualizations and multiple sequence alignments that have been reviewed recently by others [24, 25, 95]. In this context, visualization is most commonly used for exploration (hypothesis generation). Common visualization methods in addition to network visualization include scatter plots, profile plots/parallel coordinates and heatmaps with dendograms, while many tools provide combinations of those as linked views. Comprehensive summaries of visualization tools exist for cer- tain areas. Nielsen et al. [25] present a review on tools for visualizing genomes, in particular tools for visualizing sequencing data, genome browsers and com- parative genomics. Gehlenborg et al. [24] present a table of visualization tools in the area of systems biology, categorized by the different focusses of omics data. While most tools still lack in usability and integration, some of the listed tools already provide sophisticated interactive possibilities like annotating, comparing and showing confidence measures and prediction results next to view manipu- lations such as navigating, zooming and filtering. There is also a trend towards implementing web-based solutions to facilitate collaboration.
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Evaluation of a Biomedical Informatics course for medical students: a Pre-posttest study at UNAM Faculty of Medicine in Mexico

Evaluation of a Biomedical Informatics course for medical students: a Pre-posttest study at UNAM Faculty of Medicine in Mexico

The independent variables were the BMI courses and the dependent variable BMI knowledge. We also mea- sured attitudes and opinion regarding the programs at the end of each course. For the pre-post test knowledge measurements we developed a multiple-choice question (MCQ) instrument, following Downing’s recommenda- tions for effective test development [20,21]. Items were selected from the courses’ summative exams adminis- tered in the initial two years of the program, which had acceptable psychometric characteristics and covered the courses’ content through a test blueprint obtained by consensus. The blueprint and test specifications of the pre-post test were the same as those for the BMI courses’ summative examinations, with the difference that the diagnostic pre-post test had fewer items (the pre-post test had 36 items, and the summative end-of- course exams had 60 items). Our study assessment in- strument and the summative exams developed by the BMI Department covered the same themes in equivalent content proportions. The Department of Biomedical In- formatics has an Educational Assessment Committee, integrated by six clinician teachers, four informatics pro- fessionals and five individuals with formal training in educational assessment, this group developed the tests and collected the exams’ validity and reliability evidence.
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Po-Yin Yen, PhD, RN. Research Assistant Professor Department of Biomedical Informatics The Ohio State University

Po-Yin Yen, PhD, RN. Research Assistant Professor Department of Biomedical Informatics The Ohio State University

Kraemer D, Yen P, Nichols M. An economic comparison of female sterilization of hysteroscopic tubal occlusion with laparoscopic bilateral tubal ligation. Contraception. 2009;80:254-60 Cohen AM, Hersh WR, Peterson K, and Yen P. Reducing Workload in Systematic Review Preparation Using Automated Citation Classification. Journal of American Medical Informatics Association. 2006;13(2): 206-19

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The CAP cancer protocols – a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

The CAP cancer protocols – a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

The caDSR conforms to ISO/IEC 11179 Edition 2 Parts 1– 6, and also contains NCI Center for Bioinformatics (NCICB) extensions based on NCI's goal of supporting semantic interoperability. One of the most important extensions is the linkage of caDSR-structured metadata to NCI Enterprise Vocabulary Services (EVS), in particular to the NCI Thesaurus. ISO/IEC 11179 described the use of concepts as an optional feature of object class. In caDSR, the use of concepts from controlled vocabularies is man- datory for object class, property, qualifiers and representa- tion terms and optional for value meanings. Some values are not linked to concept codes because the instance data is unstructured text or simply not suitable as a concept in the NCI Thesaurus. Binding these ISO/IEC 11179 seman- tic components to concepts in the NCI Thesaurus endows a tenable layer of semantics to the already rich structure of caDSR metadata by virtue of the ontologic description (NCI Thesaurus) and mappings to synonyms in over 50 other biomedical terminologies (NCI Metathesaurus) associated with each concept.
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Maldonado, José A. Martínez Salvador, Begoña Boscá, Diego Robles, Montserrat. Revista: Journal of biomedical informatics, 2013, 46.

Maldonado, José A. Martínez Salvador, Begoña Boscá, Diego Robles, Montserrat. Revista: Journal of biomedical informatics, 2013, 46.

We are concerned with the use of archetypes within CDSSs as a standard- ised mechanism for the interaction with the EHR, in order to obtain CDSSs that can be shared across institutions without the need for modifications in the imple- mentation. This problem is mentioned by Sujansky as one of the heterogeneous database integration challenges in Medical Informatics [3], and is usually solved by means of abstractions that make the CDSS compatible with clinical databases from different institutions. We propose to use archetypes to build a semantically- rich VHR for this purpose. More precisely, our proposal is to develop a series of archetypes for the data/concepts that the CDSS requires, and to include references to these archetypes in the parts of the CDSS knowledge base (KB) where interac- tions with the EHR should occur. It is important to note that our interest in shared use (and reuse) is not limited to the KB as a whole but also covers the archetypes modelling the necessary clinical data/concepts.
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A Health and Biomedical Informatics Research Strategy for Scotland

A Health and Biomedical Informatics Research Strategy for Scotland

In December 2012, the eHealth Strategy Board of the Scottish Government, in collaboration with the Chief Scientist Office, convened a group under the leadership of Sir Lewis Ritchie to consider how Scotland should respond to the opportunities and challenges in health informatics research. This strategy reflects that group’s findings. It aims to build upon Scotland's widely-recognised strengths in health informatics research and to ensure we continue to set an international standard for the secure use of routinely collected patient data for research. The strategy is aligned with the Data Vision for Scotland which sets out an objective to champion and unleash across Scotland trustworthy uses of data for public benefit 2 . The strategy also seeks to align the investment by the Medical Research Council and others in a UK wide Institute for Health Informatics Research, with existing
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Biomedical Informatics and Cognitive Psychology

Biomedical Informatics and Cognitive Psychology

Informatics (3 in US, one international) • Research Informatics (1) • Translational Bioinformatics (1) • Public Health Informatics (1, begins this year) • Nursing Informatics (n[r]

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Nanoinformatics from the Biomedical Informatics Perspective

Nanoinformatics from the Biomedical Informatics Perspective

Mantas J, Ammenwerth E, Demiris G, Hasman A, Haux R, Hersh W, Hovenga E, Lun KC, Marin H, Martin- Sanchez F, Wright G; IMIA Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics. First Revision. Methods Inf Med. 2010 Jan 7;49(2):105-120.

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Mental Health Informatics

Mental Health Informatics

Over the last years, the practice of delivering mental health services has changed dramatically. Information on mental health problems is widely available online and indexed for search, mobile devices are being used in large numbers worldwide, the consumers of health services self-organise by use of social networks, medical and psychological assessment is being automated step-by-step and consultation is increasingly conducted via the Internet. Given the prevalence of mental health disorders and the burden on economies worldwide, mental health informatics should be a dedicated branch of Information and Communication Technology (ICT) or applied computer science.
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Informatics and the Challenge of Determinism

Informatics and the Challenge of Determinism

The impact of DI has been documented in empirical reports in the media and academic research studies conducted over the SLC. We have identified the challenge presented by TD which lies in two general areas: (i) understanding the nature and drivers of TD, and (ii) developing an approach to ISD to promote the positive aspects of new technologies while addressing (or at least mitigating) the negative affects. In this paper we consider informatics with a focus on IS and ISD along with the nature of TD and the challenge it presents over the SLC. We consider ISD from a socio-technical design perspective [5] [22] [23] [24] [25] [26] [27] [5] [28] and introduce open research questions, our current research, and proposed direction(s) for future research. We conclude that, while technological determinism remains a significant challenge, the implementation of new technologies presents both opportunities and threats for all stakeholders in computerised systems.
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NURSING INFORMATICS AUSTRALIA (NIA) Informatics for Nurses and Midwives STRATEGIC PLAN

NURSING INFORMATICS AUSTRALIA (NIA) Informatics for Nurses and Midwives STRATEGIC PLAN

2.1 NIA promotes educational opportunities in nursing informatics 2.2 NIA engages with stakeholders to support their education, involvement and participation in nursing informatics 2.3 NIA will encourage networking opportunities for nurse informaticians 2.4 NIA promotes research in nursing informatics

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