Medical Informatics; Biomedical Informatics; Information
Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs—even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results.
We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset.
Results and Conclusions
The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.
At the meeting of the IMIA Board in 2009 in Hiroshima, it approved an IMIA 50th Anniversary History Project to produce a historical volume and other materials to commemorate the anniversary of the foundation of the predecessor of IMIA–the IFIP-TC4 in 1967. A Taskforce was organized under the direction of Casimir Kulikowski, then the VP for Services of IMIA, and since that time it has met regularly to plan and implement the 50th Anniversary History of IMIA as an edited volume, and as material available online on a Media Presentation Database. The IMIA Taskforce is gathering IMIA-related archival materials, currently accessible through a prototype media repository at Rutgers University in order to help those contributing to the book or writing their own recollections and histories. The materials will support a chronicle of the development and evolution of IMIA, its contributors, its sponsored events and publications, educational and other professional activities. During 2013 Workshops were held at the Prague EFMI-STC meeting in April and at the MEDINFO 2013 Congress in Copenhagen in August.
Medical Informatics; Medical History; History; Informatics for the History of IMIA.
The panel intended to collect data, opinions and views for a systematic and multiaxial approach for a comprehensive presentation of “History of Medical Informatics”, treating both general (global) characteristics, but emphasizing the particular features for Europe. The topic was not only a subject of large interest but also of great importance in preparing a detailed material for celebration of forty years of medical informatics in Europe. The panel comprised a list of topics, trying to cover all major aspects to be discussed. Proposals of staging the major periods of medical informatics history were also discussed.
medical informatics; health informatics; e-health history; medical informatics education; EFMI; IMIA.
The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is ‘the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.’ Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.
President and CEO; preparedness; wireless; preferences; population health; primary care; collaborative technologies; knowledge representations; knowledge acquisition and knowledge management; controlled terminologies and vocabularies; ontologies; AMIA
Over the next 10 years, more information and communication technology (ICT) will be deployed in the health system than in its entire previous history. Systems will be larger in scope, more complex, and move from regional to national and supranational scale. Yet we are at roughly the same place the aviation industry was in the 1950s with respect to system safety. Even if ICT harm rates do not increase, increased ICT use will increase the absolute number of ICT related harms. Factors that could diminish ICT harm include adoption of common standards, technology maturity, better system development, testing, implementation and end user training. Factors that will increase harm rates include complexity and heterogeneity of systems and their interfaces, rapid implementation and poor training of users. Mitigating these harms will not be easy, as organizational inertia is likely to generate a hysteresis-like lag, where the paths to increase and decrease harm are not identical.
Cognitive study (including experiments emphasizing verbal protocol analysis and usability); collaborative technologies; communication; decision support; developing/using computerized provider order entry; ethical study methods; historical; human–computer interaction and human-centered computing; legal; policy; qualitative/ethnographic field study; safety; social/organizational study; surveys and needs analysis; system implementation and management issues
Over the past years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for the Medical Informatics (MI) field, so that locating and accessing them currently remains a difficult and time-consuming task.
We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. We define informatics resources as all those elements that constitute, serve to define or are used by informatics systems, ranging from architectures or development methodologies to terminologies, vocabularies, databases or tools. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources’ names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different classification schemas by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the classification schemas. The classification algorithm identifies the categories associated with resources and annotates them accordingly. The database is then populated with this data after manual curation and validation.
We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contains 609 resources at the time of writing and is available at http://www.gib.fi.upm.es/eMIR2. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers.
Medical informatics; Cataloging; Classification; Software resources; Information storage and retrieval; Search engine; Database; Information management; Folksonomies; Social tagging
Over a decade ago, nanotechnologists began research on applications of nanomaterials for medicine. This research has revealed a wide range of different challenges, as well as many opportunities. Some of these challenges are strongly related to informatics issues, dealing, for instance, with the management and integration of heterogeneous information, defining nomenclatures, taxonomies and classifications for various types of nanomaterials, and research on new modeling and simulation techniques for nanoparticles. Nanoinformatics has recently emerged in the USA and Europe to address these issues. In this paper, we present a review of nanoinformatics, describing its origins, the problems it addresses, areas of interest, and examples of current research initiatives and informatics resources. We suggest that nanoinformatics could accelerate research and development in nanomedicine, as has occurred in the past in other fields. For instance, biomedical informatics served as a fundamental catalyst for the Human Genome Project, and other genomic and –omics projects, as well as the translational efforts that link resulting molecular-level research to clinical problems and findings.
biomedical informatics; nanomedicine; nanotoxicology; ontologies; electronic health records
Accurate tracking of tumor movement in fluoroscopic video sequences is a clinically significant and challenging problem. This is due to blurred appearance, unclear deforming shape, complicate intra- and inter- fractional motion, and other facts. Current offline tracking approaches are not adequate because they lack adaptivity and often require a large amount of manual labeling. In this paper, we present a collaborative tracking algorithm using asymmetric online boosting and adaptive appearance model. The method was applied to track the motion of lung tumors in fluoroscopic sequences provided by radiation oncologists. Our experimental results demonstrate the advantages of the method.
Online Learning; Contour Tracking; Fluoroscopy
The encoding of clinical practice guidelines into machine operable representations poses numerous challenges and will require considerable human intervention for the foreseeable future. To assist and potentially speed up this process, we have developed an incremental approach to guideline encoding which begins with the annotation of the original guideline text using markup techniques. A modular and flexible sequence of subtasks results in increasingly inter-operable representations while maintaining the connections to all prior source representations and supporting knowledge. To reduce the encoding bottleneck we also employ a number of machine-assisted learning and prediction techniques within a knowledge-based software environment. Promising results with a straightforward incremental learning algorithm illustrate the feasibility of such an approach.
As part of a larger effort to automate guidelines we determined the number
and types of clinical variables required to implement two complex
clinical guidelines and the adequacy of the electronic medical record (EMR) to
capture them. 178 unique variables were required by both guidelines. Variables
were classified as simple (existing observation terms
in the EMR), calculated (transformations of simple variables), and complex (requiring
multiple simple variables and logical rules for combining
them). Many variables are unlikely to be instantiated in an EMR
without focused efforts to collect them. In addition, many variables required
knowledge that was neither provided in the guideline nor referenced. We
conclude that, although the EMR contains the necessary variables
to implement these guidelines, successful automated implementation
requires unambiguous definition of required terms, incorporation of
additional knowledge not provided in the guideline and modification of
workflow to collect variables not normally captured in routine clinical
Practice Guidelines; Decision Support Systems; Clinical; vocabulary; controlled; terminology
In this report, the authors compare and contrast medical informatics (MI) and bioinformatics (BI) and provide a viewpoint on their complementarities and potential for collaboration in various subfields. The authors compare MI and BI along several dimensions, including: (1) historical development of the disciplines, (2) their scientific foundations, (3) data quality and analysis, (4) integration of knowledge and databases, (5) informatics tools to support practice, (6) informatics methods to support research (signal processing, imaging and vision, and computational modeling, (7) professional and patient continuing education, and (8) education and training. It is pointed out that, while the two disciplines differ in their histories, scientific foundations, and methodologic approaches to research in various areas, they nevertheless share methods and tools, which provides a basis for exchange of experience in their different applications. MI expertise in developing health care applications and the strength of BI in biological “discovery science” complement each other well. The new field of biomedical informatics (BMI) holds great promise for developing informatics methods that will be crucial in the development of genomic medicine. The future of BMI will be influenced strongly by whether significant advances in clinical practice and biomedical research come about from separate efforts in MI and BI, or from emerging, hybrid informatics subdisciplines at their interface.
Peer-reviewed publication of scientific research results represents the most important means of their communication. The authors have annually reviewed a large heterogeneous set of papers to produce the International Medical Informatics Association (IMIA) Yearbook of Medical Informatics. To support an objective and high-quality review process, the authors attempted to provide reviewers with a set of refined quality criteria, comprised of 80 general criteria and an additional 60 criteria for specific types of manuscripts. Authors conducted a randomized controlled trial, with 18 reviewers, to evaluate application of the refined criteria on review outcomes. Whereas the trial found that reviewers applying the criteria graded papers more strictly (lower overall scores), and that junior reviewers appreciated the availability of the criteria, there was no overall change in the interrater variability in reviewing the manuscripts. The authors describe their experience as a “case report” and provide a reference to the refined quality review criteria without claiming that the criteria represent a validated instrument for quantitative quality measurement.
The authors present the case study of a 35-year
informatics-based single subspecialty practice for the management of patients
with chronic thyroid disease. This extensive experience provides a paradigm
for the organization of longitudinal medical information by integrating
individual patient care with clinical research and education. The kernel of
the process is a set of worksheets easily completed by the physician during
the patient encounter. It is a structured medical record that has been
computerized since 1972, enabling analysis of different groups of patients to
answer questions about chronic conditions and the effects of therapeutic
interventions. The recording process and resulting studies severe as an
important vehicle for medical education about the nuances of clinical
practice. The authors suggest ways in which computerized medical records can
become an integral part of medical practice, rather than a luxury or
A decade ago, the term knowledge-based system was just beginning to be used to characterize a set of prototype systems that relied for their reasoning on detailed knowledge of a domain. Biomedical problems proved particularly useful in demonstrating both the feasibility and advantage of this new approach. Today, knowledge-based systems cover a wide class of classification, interpretation, planning, and design problems, and span almost every conceivable domain of science and engineering, as well as business and management. Biomedicine, as a domain, continues to provide a fertile source of new and challenging problems. Advances in explanatory reasoning, qualitative models of biomedical processes, image interpretation, knowledge base learning and refinement, decision analytic and heuristic methods of reasoning under uncertainty, experiment planning/design, and extensive technological transfer results, all attest to the vitality of the knowledge-based approach, placing it at the forefront of biomedical computing research.
A generalized scheme for building consultation systems based on techniques of artificial intelligence (A.I.) was used to construct a sequence of thyroid consultation models. This scheme, called EXPERT , provided a language in which the decision making elements and rules of the clinical expert were defined, compiled, and tested against a data base of cases. In the present paper we report on the incremental process of refining the original model through repeated cycles of empirical testing, re-definition, and re-testing. This process was facilitated by the development of programs that interfaced the EXPERT system with the independent thyroid data base, and analyzed performance, thus enabling a rapid assessment of the effect of changes in the decision making rules.
In developing computer based medical consultation systems the choice of representation for the knowledge base crucially determines the scope, power and flexibility of the reasoning procedures that can be used with it. This paper describes the development of the CASNET/Glaucoma consultation system and discusses some concepts of knowledge base structure that arose during this process.