Search

Though women increasingly make up the majority of medical-school and other science graduates, they remain a minority in academic biomedical settings, where they are less likely to hold leadership positions or be awarded research funding. A major factor is the career breaks that women disproportionately take to see to familial duties. They experience a related, but overlooked, hurdle upon their return: they are often too old to be eligible for 'early-career researcher' grants and 'career-development' awards, which are stepping stones to leadership positions in many institutions and which determine the demographics of their hierarchies for decades to come. Though age limits are imposed to protect young applicants from more experienced seniors, they have an unintended side effect of excluding returning workers, still disproportionately women, from the running. In this joint effort by the European Society of Clinical Microbiology and Infectious Diseases, the Federation of European Microbiological Societies, the Infectious Disease Society of America, the International Society for Infectious Diseases and the Swiss Society for Infectious Diseases, we invited all European Congress of Clinical Microbiology and Infectious Diseases-affiliated medical societies and funding bodies to participate in a survey on current 'early-career' application restrictions and measures taken to provide protections for career breaks. The following simple consensus recommendations are geared to funding bodies, academic societies and other organizations for the fair handling of eligibility for early-career awards: 1. Apply a professional, not physiological, age limit to applicants. 2. State clearly in the award announcement that career breaks will be factored into applicants' evaluations such that: • Time absent is time extended : for every full-time equivalent of career break taken, the same full-time equivalent will be extended to the professional age limit. • Opportunity costs will also be taken into account : people who take career breaks risk additional opportunity costs, with work that they did before the career break often being forgotten or poorly documented, particularly in bibliometric accounting. Although there is no standardized metric to measure additional opportunity costs, organizations should (a) keep in mind their existence when judging applicants' submissions, and (b) note clearly in the award announcement that opportunity costs of career breaks are also taken into account. 3. State clearly that further considerations can be undertaken, using more individualized criteria that are specific to the applicant population and the award in question. The working group welcomes feedback so that these recommendations can be improved and updated as needed. [ABSTRACT FROM AUTHOR]

• medBERT.de : German BERT model tailored for medical domain expertise. • Trained using 4.7 million diverse medical documents for optimal results. • Achieved new state-of-the-art on eight medical NLP benchmarks. • Efficient tokenization has minor role, focus on domain-specific training. • Public release of pre-trained model weights and new benchmarks for research. This paper presents medBERT.de , a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERT.de are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community. [ABSTRACT FROM AUTHOR]

The article presents abstracts on medical topics which include the molecular mechanism of Hypoxia-inducible factor-1 independent vascular endothelial growth factor expression in hepatocellular carcinoma cell line, anti-inflammatory effects of the nigella sativa seed extract, thymoquinone, in pancreatic cancer cells, and a proteomic based platform for marker discovery in cholangiocarcinoma.

Presents abstracts of medical studies focusing on organ transplantation and diseases of the liver and pancreas. "A Study on Human Liver ICAM-1 Expression and Its Correlation With Post-Transplant Acute Cellular Rejection," by M. El-Wahsh, B. Fuller, K. Rolles, and B. Davidson; "Is the Use of Ischaemic Preconditioning Worthwhile in Liver Transplantation?" by F. Berrevoet, T. Schäfer, B. Vollmar and M. Menger; "Relationship of Necrosis to Organ Failure in Patients With Acute Pancreatitis," by D. V. Radenkovic, D. J. D. Bajec, N. D. J. Ivancevic, and A. R. Karamarkovic.

"The Journal of Investigative Dermatology" had another successful year in 1990, as exemplified by growing submission and publication of a large and varied journal of high-quality papers. A new category, the "High impact" paper, has been introduced in order to identify particular important papers early in the review process and hasten those papers to publication. the time for submission to decision on these papers is under one month, if authors complete revisions rapidly, these papers can be published in four months from submission.

New technologies are transforming medicine, and this revolution starts with data. Health data, clinical images, genome sequences, data on prescribed therapies and results obtained, data that each of us has helped to create. Although the first uses of artificial intelligence (AI) in medicine date back to the 1980s, it is only with the beginning of the new millennium that there has been an explosion of interest in this sector worldwide. We are therefore witnessing the exponential growth of health-related information with the result that traditional analysis techniques are not suitable for satisfactorily management of this vast amount of data. AI applications (especially Deep Learning), on the other hand, are naturally predisposed to cope with this explosion of data, as they always work better as the amount of training data increases, a phase necessary to build the optimal neural network for a given clinical problem. This paper proposes a comprehensive and in-depth study of Deep Learning methodologies and applications in medicine. An in-depth analysis of the literature is presented; how, where and why Deep Learning models are applied in medicine are discussed and reviewed. Finally, current challenges and future research directions are outlined and analysed. • We review the state-of-the-art focusing on the application of DL in medicine. • We expose a categorization of Deep Learning models used and applied in medicine. • We classify medicine-related DL applications into macro-areas and sub-areas. • We thoroughly discuss the recent and open challenges related to DL in medicine. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference "Fair medicine and AI" (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation. • Bias, discrimination and structural injustice for medical AI are an overlooked issue. • AI for biomedical research and healthcare should be beneficent and equitable. • Social science perspectives within AI for medicine development are essential. • Challenges are multifold and an agenda for future research is needed. • Qualitative, ethnographic and participatory approaches could help provide fairer AI. [ABSTRACT FROM AUTHOR]

Medical use cases for machine learning (ML) are growing exponentially. The first hospitals are already using ML systems as decision support systems in their daily routine. At the same time, most ML systems are still opaque and it is not clear how these systems arrive at their predictions. In this paper, we provide a brief overview of the taxonomy of explainability methods and review popular methods. In addition, we conduct a systematic literature search on PubMed to investigate which explainable artificial intelligence (XAI) methods are used in 450 specific medical supervised ML use cases, how the use of XAI methods has emerged recently, and how the precision of describing ML pipelines has evolved over the past 20 years. A large fraction of publications with ML use cases do not use XAI methods at all to explain ML predictions. However, when XAI methods are used, open-source and model-agnostic explanation methods are more commonly used, with SHapley Additive exPlanations (SHAP) and Gradient Class Activation Mapping (Grad-CAM) for tabular and image data leading the way. ML pipelines have been described in increasing detail and uniformity in recent years. However, the willingness to share data and code has stagnated at about one-quarter. XAI methods are mainly used when their application requires little effort. The homogenization of reports in ML use cases facilitates the comparability of work and should be advanced in the coming years. Experts who can mediate between the worlds of informatics and medicine will become more and more in demand when using ML systems due to the high complexity of the domain. [Display omitted] • We estimate that only 16 % of the reported explainability methods could be understood by patients. • The distribution of data types for explainable ML applications for tabular | image | text | audio data is 51 % | 32 % | 3 % | 0 %. • The quality of the description of machine learning pipelines increased in recent years with more homogeneity. • The data and code sharing ratio stagnated in about one quarter. • Most popular explainability methods are SHAP, LIME, and Grad-CAM. [ABSTRACT FROM AUTHOR]

Over the last two decades, augmented reality (AR) has led to the rapid development of new interfaces in various fields of social and technological application domains. One such domain is medicine, and to a higher extent surgery, where these visualization techniques help to improve the effectiveness of preoperative and intraoperative procedures. Following this trend, this paper presents SLIMBRAIN, a real-time acquisition and processing AR system suitable to classify and display brain tumor tissue from hyperspectral (HS) information. This system captures and processes HS images at 14 frames per second (FPS) during the course of a tumor resection operation to detect and delimit cancer tissue at the same time the neurosurgeon operates. The result is represented in an AR visualization where the classification results are overlapped with the RGB point cloud captured by a LiDAR camera. This representation allows natural navigation of the scene at the same time it is captured and processed, improving the visualization and hence effectiveness of the HS technology to delimit tumors. The whole system has been verified in real brain tumor resection operations. [ABSTRACT FROM AUTHOR]

• Daily or circadian rhythms are physiological and behavioural rhythms that occur over a roughly 24-hour period. • These bodily rhythms have been largely overlooked by historians of health and medicine. • How circadian rhythms are understood to affect health and disease varies across cultural and temporal contexts. • Henri Lefebvre's concept of rhythmanalysis offers a methodology for new research in 'rhythmic history'. Rhythm characterizes life on Earth. Daily physiological rhythms of eating and fasting, sleeping and waking, moving and resting, are common to almost all life forms which evolved under the solar light–dark cycle. Despite their ubiquity, historians of health and medicine have yet to grapple with the lived experiences of these daily rhythms in the past. This paper presents a potential new research agenda in 'rhythmic history' that understands rhythmicity as something which lies between biology and culture. Thinking with rhythms offers exciting opportunities to unite previously disparate historical studies of daily rhythms like eating and sleeping and opens up a new way to view the enmeshed connections between body and environment. In this paper, I take inspiration from the scientific concept of the 'zeitgeber' ('time giver'), coined by the German chronobiologist Jürgen Aschoff, to frame a review of current literature relating to rhythms and explore Henry Lefebvre's notion of 'rhythmanalysis' as a methodological tool for historians undertaking 'rhythmic histories'. [ABSTRACT FROM AUTHOR]

Highlights • Models for diagnosis aid may provoke distrust among practitioners and patients. • The development of models for diagnosis aid falls under data mining concerns. • The survey raises an expert-aware approach for suitable diagnosis aid. • Interpretability is a major concern for the acceptance of diagnosis aid models. • The recourse to data mining for diagnosis aid poses many technical challenges. Abstract Data mining continues to play an important role in medicine; specifically, for the development of diagnosis aid models used in expert and intelligent systems. Although we can find abundant research on this topic, clinicians remain reluctant to use decision support tools. Social pressure explains partly this lukewarm position, but concerns about reliability and credibility are also put forward. To address this reticence, we emphasize the importance of the collaboration between both data miners and clinicians. This survey lays the foundation for such an interaction, by focusing on the specifics of diagnosis aid, and the related data modeling goals. On this regard, we propose an overview on the requirements expected by the clinicians, who are both the experts and the final users. Indeed, we believe that the interaction with clinicians should take place from the very first steps of the process and throughout the development of the predictive models, thus not only at the final validation stage. Actually, against a current research approach quite blindly driven by data, we advocate the need for a new expert-aware approach. This survey paper provides guidelines to contribute to the design of daily helpful diagnosis aid systems. [ABSTRACT FROM AUTHOR]

Abstract Academic discourse is characterised by being 'enormously diverse' (Hyland, 2004) because of its different disciplines and genres. Authors of academic texts are influenced by their disciplines, and by the 'discourse formats' (Fløttum et al., 2006) of what they are writing. However, genres exist across disciplines. For instance, research articles and PhD theses are found in different disciplines, namely medicine and sociology. Added to that, Systemic Functional Linguistics proposes that lexicogrammatical choices are activated by the context, which is, in this case, multiple. This paper seeks to study how the different parameters of context might influence the choices of PPs. A corpus of PhD theses and research articles from medicine and sociology is collected, and a sample is extracted and annotated via CorpusTool. The result revealed that certain choices reveal that while research articles are more similar across the disciplines, PhD theses are less restricted and disciplinary and personal differences are prominent. Highlights • Linguistic choices of Prepositional Phrases can be related to the different parameters of context in academic writing. • Certain lexico-grammatical choices are global: they reveal features of the field of academic writing, in general. • Other choices distinguish the different types of context: the genres and disciplines studied. • The boundary between genre and discipline is not fixed; it depends on the genre itself, as certain genres (Research Articles) have more restrictions than others (PhD Theses). [ABSTRACT FROM AUTHOR]

Abstract This paper represents an innovative technique to cure some of the fatal diseases such as rheumatoid arthritis and tumors by injected nano-size gold particles (gold salt in the veins through a hotted slim tube. In the proposed model large atomic number enables gold particles in attaining further heat and conveys medicine to the effected organs and erodes malign tissues or cells. The couple stress fluid is treated as blood inside the two coaxial tubes containing nanoparticles. The inner tube is considered to be rigid in configuration while the outer is elastic or flexible to eventuate a sinusoidal wave once the fluid passes within the gap of tubes. A set of nonlinear and coupled mathematical equations governing the flow model has been converted into a system of ordinary differential equations using suitable transformations prior to seeking an analytical solutions. A comprehensive parametric study has also been made to make the presented analysis more realistic to ensure that the presented study adheres with the physical phenomenon. Effects of velocity, temperature and concentration of the particles are examined corresponding to variate physical parameters contributing in the flow. Besides, highlighting graphically, the impact of each sundry parameters has been explained in detail at the end. It is worth mentioning that the results for Newtonian fluid can be obtained from the reported results by taking couple stress parameter is zero. Highlights • An innovative technique to cure fatal diseases such as rheumatoid arthritis and tumors is proposed. • Nano-size gold particles (gold salt in the veins through a hotted slim tube) are injected. • The couple stress fluid as blood inside the two coaxial tubes is considered. • Results for Newtonian fluid can be obtained as a limiting case of the considered problem. [ABSTRACT FROM AUTHOR]

Background: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.Objectives: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.Methods: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.Results: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.Conclusions: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper reviews data mining application in medicine from 1999 to 2013. [•] Medical data mining definition and its main goals and challenges are clarified. [•] Comprehensive categorization is presented to complete previous works and integrate medical and data mining issues. [•] Each paper is studied from six medical tasks and in each tasks, five data mining approaches are considered. [•] A discussion deals with previous research outline and future trend. [Copyright &y& Elsevier]

An abstract of a paper on nucleoside diphosphate kinase is presented.

This paper investigates the effects of shiphandling manoeuvres on mental workload and physiological reactions in ten marine pilots. Each pilot performed four berthings in a ship simulator. Those berthings were differentiated by two factors, level of difficulty and familiarity with the port. Each berthing could also be divided into five phases, three during the execution and two resting periods, one before and one after the execution (dedicated to baseline physiological data collection). Mental workload was measured through two self assessment scales: the NASA TLX and a Likert scale. Power spectral densities on Beta bands 1 and 2 were obtained from EEG. Heart rate and heart rate variability were obtained from ECG. Pupil dilation was obtained from eye tracking. Workload levels were higher as berthings increased in difficulty level and/or the pilots completed the berthings in unfamiliar ports. Responses differed across specific phases of the berthings. Physiological responses could indirectly monitor levels of mental workload, and could be adopted in future applications to evaluate training improvements and performance. This study provides an example of an applied methodology aiming to define an upper redline of task demands in the context of marine pilotage. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a new condition ( C ) α ∫ 0 u f ( s ) d s ≤ u f ( u ) + β u 2 + γ , u > 0 for some α , β , γ > 0 with 0 < β ≤ α − 2 λ 0 2 , where λ 0 is the first eigenvalue of discrete Laplacian Δ ω , with which we obtain blow-up solutions to discrete semilinear heat equations u t x , t = Δ ω u x , t + f ( u ( x , t ) ) , x , t ∈ S × 0 , + ∞ , u x , t = 0 , x , t ∈ ∂ S × 0 , + ∞ , u x , 0 = u 0 ≥ 0 ( n o n t r i v i a l ) , x ∈ S ¯ on a discrete network S . In fact, it will be seen that the condition (C) improves the conditions known so far. [ABSTRACT FROM AUTHOR]

Micro/nanomotors (MNMs) that could perform diverse tasks are the most dynamic research areas in nanoscience and nanotechnology. Up to now, the majority of micromotors contain the precious metal platinum (Pt) as the catalyst for propulsion. The high cost and scarcity of Pt seriously impede its practical application. The exploration of manganese oxide based MNMs provides a route to circumstance the drawbacks of Pt. In this paper, graphene/MnO 2 microtubes as a highly efficient micromotor was fabricated by a template-assisted electrochemical process. The resulted micromotors show a good motion behavior in a wide range of hydrogen peroxide (H 2 O 2 ) concentrations. Owing to its robust motion, easy functionalization, a large quantity of surface functional groups for modification, and simple synthesis, these fine micromotors hold a great promise for biomedicine and environmental applications. [ABSTRACT FROM AUTHOR]

Responsive hydrogels have been extensively studied in the past decades because they are able to interact with their biological environment in a pre-programmed manner. Several biomedical applications have already been achieved (or at least approached) by in vivo experiments. As a class, injectable hydrogels gained considerable attention because of their minimally invasive implantation. However, the final shaping of hydrogel implants is not resolved, and their lifetime is limited because of their insufficient mechanical stability. The solution to these challenges can be given by two seemingly independent properties, shape memory and self-healing. Both properties are well-known for conventional polymers but research on the shape memory or self-healing of hydrogels is in its infancy. In this study, we introduce the molecular mechanisms behind these two properties with a focus on hydrogels, attempt to provide a general overview on the role of reversible physical and chemical interactions, and discuss the similarities between the background of shape memory and self-healing. There are a number of open questions regarding the uniform characterisation of such hydrogels, and their theoretical description is very incomplete, but the developed systems hold great promise for future applications. In the final part of the paper, we note that the synthesis of hydrogels providing both self-healing and shape memory is a difficult challenge, but some examples do exist. Future research in these fields should focus on a better understanding of structure-property correlations and should uncover additional fields of application for these advanced materials. [ABSTRACT FROM AUTHOR]

Abstract: Laughter is employed in social interaction to construct meaning, identities, and relationships. Although studies have begun to examine humour within the medical workplace, none have employed observational methods to study laughables/laughter within the learning triad of the bedside teaching encounter (BTE). This paper focuses on this gap by exploring the novel question: How do students, patients, and doctors construct and co-construct power, identity and gender through laughter within BTEs? In this paper, we focus on the disaffiliative function of laughables/laughter across BTEs. Most of the laughables presented can be construed as teases: fallibility, frustration, cynicism and/or sexual teasing; and this teasing was accompanied by a competitive rather than collaborative play frame. Teasing and laughter was employed by participants to maintain or subvert existing power asymmetries, to construct identities, for example, as individuals who are intelligent, witty and powerful, and to construct gender by performing masculinity or femininity. Further research with a larger and more diverse sample of participants (including more female doctors, male students and female patients) is now essential to explore more fully the co-construction of power, identities and gender through laughter within medical workplace learning encounters. [ABSTRACT FROM AUTHOR]

Summary: Objective: Medical data is often very high dimensional. Depending upon the use, some data dimensions might be more relevant than others. In processing medical data, choosing the optimal subset of features is such important, not only to reduce the processing cost but also to improve the usefulness of the model built from the selected data. This paper presents a data mining study of medical data with fuzzy modeling methods that use feature subsets selected by some indices/methods. Methods: Specifically, three fuzzy modeling methods including the fuzzy k-nearest neighbor algorithm, a fuzzy clustering-based modeling, and the adaptive network-based fuzzy inference system are employed. For feature selection, a total of 11 indices/methods are used. Medical data mined include the Wisconsin breast cancer dataset and the Pima Indians diabetes dataset. The classification accuracy and computational time are reported. To show how good the best performer is, the globally optimal was also found by carrying out an exhaustive testing of all possible combinations of feature subsets with three features. Results: For the Wisconsin breast cancer dataset, the best accuracy of 97.17% was obtained, which is only 0.25% lower than that was obtained by exhaustive testing. For the Pima Indians diabetes dataset, the best accuracy of 77.65% was obtained, which is only 0.13% lower than that obtained by exhaustive testing. Conclusion: This paper has shown that feature selection is important to mining medical data for reducing processing time and for increasing classification accuracy. However, not all combinations of feature selection and modeling methods are equally effective and the best combination is often data-dependent, as supported by the breast cancer and diabetes data analyzed in this paper. [Copyright &y& Elsevier]

Abstract: In these two companion papers, we provide an overview and a brief history of the multiple roots, current developments and recent advances of integrative systems biology and identify multiscale integration as its grand challenge. Then we introduce the fundamental principles and the successive steps that have been followed in the construction of the scale relativity theory, which aims at describing the effects of a non-differentiable and fractal (i.e., explicitly scale dependent) geometry of space–time. The first paper of this series was devoted, in this new framework, to the construction from first principles of scale laws of increasing complexity, and to the discussion of some tentative applications of these laws to biological systems. In this second review and perspective paper, we describe the effects induced by the internal fractal structures of trajectories on motion in standard space. Their main consequence is the transformation of classical dynamics into a generalized, quantum-like self-organized dynamics. A Schrödinger-type equation is derived as an integral of the geodesic equation in a fractal space. We then indicate how gauge fields can be constructed from a geometric re-interpretation of gauge transformations as scale transformations in fractal space–time. Finally, we introduce a new tentative development of the theory, in which quantum laws would hold also in scale space, introducing complexergy as a measure of organizational complexity. Initial possible applications of this extended framework to the processes of morphogenesis and the emergence of prokaryotic and eukaryotic cellular structures are discussed. Having founded elements of the evolutionary, developmental, biochemical and cellular theories on the first principles of scale relativity theory, we introduce proposals for the construction of an integrative theory of life and for the design and implementation of novel macroscopic quantum-type experiments and devices, and discuss their potential applications for the analysis, engineering and management of physical and biological systems and properties, and the consequences for the organization of transdisciplinary research and the scientific curriculum in the context of the SYSTEMOSCOPE Consortium research and development agenda. [Copyright &y& Elsevier]

Summary: Objective: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. Background: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. Methodology: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. Results: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader—including those who are unfamiliar with the topic—to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. Conclusions: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research. [Copyright &y& Elsevier]

Summary: Purpose: Over recent years, there has been a growing interest in extracting entities and relations from biomedical literature. There are a vast number of systems and approaches being proposed to extract biological relations, but none of them achieves satisfactory results. These methodologies are either parsing-based or pattern-based, which are not competent to handle the grammatical complexities of biomedical texts, or too complicated to be adapted. It is well known that appositive, coordinative propositions and such grammatical structures are extremely common in biomedical texts, particularly in full texts. However, these problems are still untouched for most of researchers. Methods: In this paper, we have proposed a new approach, which is hybrid with both shallow parsing and pattern matching, to extract relations between proteins from scientific papers of biomedical themes. In the method, appositive and coordinative structures are interpreted based on the shallow parsing analysis, with both syntactic and semantic constraints. Then long sentences are splitted into sub-ones, from which relations are extracted by a greedy pattern matching algorithm, along with automatically generated patterns. Results: Our approach is experimented to extract protein–protein interactions from full biomedical texts, and has achieved an average F-score of 80% on individual verbs, and 66% on all verbs. With the help of shallow parsing analysis, pattern matching is improved remarkably. Compared with the traditional pattern matching algorithm, our approach achieves about 7% improvement of both precision and F-score. In contrast to other systems, our approach achieves performance comparable to the best. A demo system has been available at http://spies.cs.tsinghua.edu.cn. [Copyright &y& Elsevier]

Summary: Clinical Practice Guidelines (CPGs) are being developed as a tool to promote best practice in medicine. However, the diffusion of paper guidelines has been shown to only have a limited impact. This is why computerization of CPGs has recently been suggested as a means to improve their dissemination as well as physicians’ compliance. The Guideline Elements Model (GEM) has been proposed to facilitate the encoding of CPGs and support the automatic processing of marked-up documents. In this paper, we explore the automatic generation of a rule base from a textual guideline using GEM. In this study, we propose an extension of the GEM model that introduces additional levels of structuring centered on decision variables. This allows a more efficient representation of the decision processes, which supports the automatic generation of decision rules from textual guidelines. The 1999 Canadian recommendations for the management of hypertension have been marked-up as a GEM-encoded instance of our extended DTD. We derived a rule base using an XML parser to extract the relevant elements to instantiate the IF and THEN clauses of decision rules. The rule base automatically generated compares favourably with the manual generation of decision rules in the ASTI project. This approach is an interesting case study in the computerization of CPGs, as it illustrates processing steps that are relevant to the various aspects of CPGs lifecycle, from production to consultation and use. [Copyright &y& Elsevier]

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily. [Copyright &y& Elsevier]

A World Health Organisation survey indicated that about 70–80% of the world populations rely on non-conventional medicine mainly of herbal sources in their primary healthcare. In recent years, we have witnessed the increasing growth in popularity of over-the-counter (OTC) health foods, nutraceuticals, and medicinal products from plants or other natural sources in developed countries. This indirectly indicates that the public is not satisfied with their orthodox medical (OM) treatment. Such increase in popularity has also brought concerns and fears over the professionalism of practitioners, and quality, efficacy and safety of their treatment methods and products from herbal and natural sources available in the market. Over the past decade several news-catching episodes in developed communities indicated adverse effects, sometimes life threatening, allegedly arisen consequential to taking of OTC herbal products or traditional medicines from various ethnic groups. These OTC products may be contaminated with excessive or banned pesticides, microbial contaminants, heavy metals, chemical toxins, and for adulterated with orthodox drugs. Excessive or banned pesticides, heavy metals and microbial contaminants may be related to the source of these herbal materials, if they are grown under contaminated environment or during collection of these plant materials. Chemical toxins may come from unfavourable or wrong storage conditions or chemical treatment due to storage. The presence of orthodox drugs can be related to unprofessional practice of manufacturers. Some of these environment related factors can be controlled by implementing standard operating procedures (SOP) leading to Good Agricultural Practice (GAP), Good Laboratory Practice (GLP), Good Supply Practice (GSP) and Good Manufacturing Practice (GMP) for producing these medicinal products from herbal or natural sources. The public’s belief that herbal and natural products are safer than synthetic medicines can only be ascertained by imposing regulatory standards on these products that should be manufactured using these Good Practices.Using Chinese medicines, as examples, this paper illustrate how advances in chemical and biomedical analysis would help to detect intentional and unintentional toxic contaminants in herbal substances. The paper also summarises how modernization and progress are being carried out to get the best out of Chinese medicines for public healthcare. [Copyright &y& Elsevier]

The medical procedures at the patient bedside are out of the scope of current HIS/RIS systems, which means that patient record and image data access during the medical visit or the execution, recording and confirmation of the medicine prescriptions, still do not enjoy computerized support. As a consequence, the necessary inclusion of new data to the patient record, still needs to be carried out through notations on paper and later typed, causes delays on the availability of this information (Mobile computing in a hospital: the Ward-In-Hand project. SAC 2000-ACM Symposium on Applied Computing, Marc¸o, 2000, Villa Olmo, Como, Italy). This paper presents a software technology where the medical and nursing staff will be equipped with a handheld computer connected by radio to a central server that provides access to the electronic patient records and that will actively inform about tasks pending distribution. The server acts as a database for multimodal electronic patient record information, storing patient data from computerized tomography (CT), ultrasonography (US), magnetic resonance (MRI) images and also medical findings, observations and prescriptions coded as DICOM Structured Reports (Digital Image and Communications in Medicine). The prototype described in this article implements a medical images and structured reports server that makes the search and recovery of data stored in the DICOM standard possible. [Copyright &y& Elsevier]

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation. [Copyright &y& Elsevier]

Bone remodeling is an evolutionary process of microstructure configuration that occurs under a variety of external stimulations. Critical to the process are the osteoblast and osteoclast cells, which relate to bone formation and absorption. Unlike osteoclast cells, the osteoblast cells are sensitive to mechanical strain, which indicates that mechanical strain plays a key role in the bone remodeling process. In this paper, a modified governing equation for bone remodeling is proposed to simulate the remodeling process at a microscale. Equivalent strain was used as the stimulus, and the influence of medicine was taken into account. A forward Euler integration method and a strain distribution solver, Abaqus/standard, were used to solve the governing equation. Three sets of simulations were conducted based on the idealized microstructure configuration, the semi-idealized microstructure configuration (the space truss structure) and the actual bone microstructure configuration derived from a Micro-CT scan experiment. The results indicate that mechanical load eventually determines the microstructure configuration as bone structure attempts to support the mechanical load with minimal mass. The simulation confirms that Denosumab, which has been wildly used to treat osteoporosis, effectively increased bone density and decreased porosity. Furthermore, bone microstructure samples were obtained through the simulation to provide the elastic module – porosity data points. All of the simulations indicated that the proposed method was reasonable and effective. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis. [ABSTRACT FROM AUTHOR]

An appreciation of the importance of interactions between microbes and multicellular organisms is currently driving research in biology and biomedicine. Many human diseases involve interactions between the host and the microbiota, so investigating the mechanisms involved is important for human health. Although microbial ecology measurements capture considerable diversity of the communities between individuals, this diversity is highly problematic for reproducible experimental animal models that seek to establish the mechanistic basis for interactions within the overall host-microbial superorganism. Conflicting experimental results may be explained away through unknown differences in the microbiota composition between vivaria or between the microenvironment of different isolated cages. In this position paper, we propose standardised criteria for stabilised and defined experimental animal microbiotas to generate reproducible models of human disease that are suitable for systematic experimentation and are reproducible across different institutions. [ABSTRACT FROM AUTHOR]

Summary The importation of infectious diseases during a mass gathering may result in outbreaks. Infectious diseases associated with mass gatherings vary depending on the type and location of the mass gathering. The annual Hajj to Makkah in Saudi Arabia is one of the largest annual religious mass gatherings in the world. Preparation for the Hajj encompasses multiple sectors to develop comprehensive plans. These plans include risk assessment, utilizing existing medical infrastructure, developing electronic and paper-based surveillance activity, and the use of information technology. In this review, we describe key features of the preparedness for the 2014 Hajj and Umra, review the recent impact of emerging viruses such as Ebola in West Africa and the Middle East respiratory syndrome coronavirus (MERS-CoV) in affected countries, and highlight the updated requirements and the required vaccines. [ABSTRACT FROM AUTHOR]

Abstract: Anatomical landmarks play an important role in many biomedical image analysis applications (e.g., registration and segmentation). Landmark detection can be computationally very expensive, especially in 3D images, because every single voxel in a region of interest may need to be evaluated. In this paper, we introduce two 3D local image descriptors which can be computed simultaneously for every voxel in a volume. Both our proposed descriptors are extensions of the DAISY descriptor, a popular descriptor that is based on the histograms of oriented gradients and was named after its daisy-flower-like configuration. Our experiments on mouse brain gene expression images indicate that our descriptors are discriminative and are able to reduce the detection errors of landmark points more than 30% when compared with SIFT-3D, an extension in 3D of SIFT (scale-invariant feature transform). We also demonstrate that our descriptors are more computationally efficient than SIFT-3D and n-SIFT (an extension SIFT in n-dimensions) for densely sampled points. Therefore, our descriptors can be used in applications that require computation of the descriptors at densely sampled points (e.g., landmark point detection or feature-based registration). [Copyright &y& Elsevier]

Background: Despite the growing interest in health data science education, it is not embedded in undergraduate medical curricula and little is known about best teaching practices. This paper presents a highly innovative course in a UK university that introduces undergraduate medical students to data science. It also discusses a study on student perspectives on the learning and teaching of health data science.Methods: The pedagogical design elements of the Data Science in Medicine course are discussed, along with its syllabus, assessment methodology and flipped classroom delivery. The course has been offered to approximately 630 students over three years. Student perspectives were investigated through three focus groups with the participation of 19 students across different study years in medicine. An experiment was conducted regarding instructor-led vs. video-based modalities of online programming labs, with the participation of 8 students.Results: The course has led to improved data competency among medical students and to a positive change in their opinions about data science. Motivating the course and showing relevance to clinical practice was one of the biggest challenges. Statistics was perceived by focus group participants as an essential data skill. Including data science in the medical curriculum was perceived as important by Year 1 students, while opinions varied between Year 4/5 participants. Video-based online labs were preferred over instructor-led online labs, and they were found to be more useful and enjoyable, without leading to any significant difference in academic performance.Conclusions: Teaching data science to undergraduate medicine students is highly desirable and feasible. We recommend including statistics in the curriculum and practical skill development through simple and clinically-relevant data science tasks, supported through video-based online labs. Further reporting on similar courses is needed, as well as larger-scale studies on student perspectives. [ABSTRACT FROM AUTHOR]

Abstract: Risk-taking is normal in everyday life if there are associated (perceived) benefits. Benefit-Risk Analysis (BRA) compares the risk of a situation to its related benefits and addresses the acceptability of the risk. Over the past years BRA in relation to food and food ingredients has gained attention. Food, and even the same food ingredient, may confer both beneficial and adverse effects. Measures directed at food safety may lead to suboptimal or insufficient levels of ingredients from a benefit perspective. In BRA, benefits and risks of food (ingredients) are assessed in one go and may conditionally be expressed into one currency. This allows the comparison of adverse and beneficial effects to be qualitative and quantitative. A BRA should help policy-makers to make more informed and balanced benefit-risk management decisions. Not allowing food benefits to occur in order to guarantee food safety is a risk management decision much the same as accepting some risk in order to achieve more benefits. BRA in food and nutrition is making progress, but difficulties remain. The field may benefit from looking across its borders to learn from other research areas. The BEPRARIBEAN project (Best Practices for Risk-Benefit Analysis: experience from out of food into food; http://en.opasnet.org/w/Bepraribean) aims to do so, by working together with Medicines, Food Microbiology, Environmental Health, Economics & Marketing-Finance and Consumer Perception. All perspectives are reviewed and subsequently integrated to identify opportunities for further development of BRA for food and food ingredients. Interesting issues that emerge are the varying degrees of risk that are deemed acceptable within the areas and the trend towards more open and participatory BRA processes. A set of 6 ‘state of the art’ papers covering the above areas and a paper integrating the separate (re)views are published in this volume. [Copyright &y& Elsevier]

Abstract: Nanofibers prepared from poly (vinyl alcohol) (PVA) using the electrospinning process have received a great deal of attention over the past decade due to their outstanding characteristics and their applicability in the field of biomedicine, coupled with their innovative preparation. However, the success of the electrospinning process depends strongly on the parameters of the source polymer solution. Of these parameters the majority of published papers routinely examine the viscosity, conductivity and surface tension, while other, indeed crucial, physical characteristics are only rarely investigated. In the present work, the emphasis was also on a comprehensive investigation of the bulk and interfacial rheology as well as on the small-angle X-ray scattering (SAXS) of PVA solutions. A detailed analysis revealed rearrangements of the molecules in the solutions depending on the polymer concentration proved by measured surface tension, determined apparent specific volume and radius of gyration. Further, we found out that smooth nanofibers are formed only from those PVA solutions, where properly firm internal polymer structures that can be oriented in the direction of an applied electric field, enable complete polymer elongation during the electrospinning. It is important to note that this is the first time that such an extensive study has been made and, our findings provide a better insight into the physical properties of the solutions for electrospinning as well as their decisive influence on the formation of the nanofibers. [Copyright &y& Elsevier]

Abstract: This paper provides an overview of the legislations and regulatory approaches currently applied to the nonclinical safety assessment of human preventive vaccine products in three ICH regions, i.e., the EU, USA, and Japan. Perspectives of the three regions with regard to the various types of toxicity studies currently considered to assess the nonclinical safety of preventive vaccines are compared and described in more detail than in published guidelines. In addition, the common issues and current challenges in nonclinical safety assessment of preventive vaccines are discussed. [Copyright &y& Elsevier]

Abstract: There are currently two major tendencies in ontology development. The first is typical of specialized knowledge areas such as genetics, biology, medicine, etc., and involves building large ontologies in order to include all the concepts in the knowledge field. In contrast, the second type of ontology development contains fewer concepts, and is more focused on the semantic completeness of concept definitions rather than on the number of concepts. As an innovative solution to both problems, this paper presents an algorithm that permits the extraction of brief ontologies from large ontologies, thus reducing the number of concepts and the semantic complexity of their definitions. In addition, this algorithm has the advantage of being a user-centered tool, capable of automatically building brief ontologies, as exemplified in a case study in the area of environmental science. [Copyright &y& Elsevier]

Abstract: Benefit–risk assessment in medicine has been a valuable tool in the regulation of medicines since the 1960s. Benefit–risk assessment takes place in multiple stages during a medicine’s life-cycle and can be conducted in a variety of ways, using methods ranging from qualitative to quantitative. Each benefit–risk assessment method is subject to its own specific strengths and limitations. Despite its widespread and long-time use, benefit–risk assessment in medicine is subject to debate and suffers from a number of limitations and is currently still under development. This state of the art review paper will discuss the various aspects and approaches to benefit–risk assessment in medicine in a chronological pathway. The review will discuss all types of benefit–risk assessment a medicinal product will undergo during its lifecycle, from Phase I clinical trials to post-marketing surveillance and health technology assessment for inclusion in public formularies. The benefit–risk profile of a drug is dynamic and differs for different indications and patient groups. In the end of this review we conclude benefit–risk analysis in medicine is a developed practice that is subject to continuous improvement and modernisation. Improvement not only in methodology, but also in cooperation between organizations can improve benefit–risk assessment. [Copyright &y& Elsevier]

In the last few years, “Design Thinking” has gained popularity – it is now seen as an exciting new paradigm for dealing with problems in sectors as far a field as IT, Business, Education and Medicine. This potential success challenges the design research community to provide unambiguous answers to two key questions: “What is the core of Design Thinking?” and “What could it bring to practitioners and organisations in other fields?”. We sketch a partial answer by considering the fundamental reasoning pattern behind design, and then looking at the core design practices of framing and frame creation. The paper ends with an exploration of the way in which these core design practices can be adopted for organisational problem solving and innovation. [Copyright &y& Elsevier]

Abstract: The paper proposes an application framework to be used for medicine assisted diagnosis based on ontology and Bayesian Network (DBNO). There are two goals: (1) to separate the domain knowledge from the probabilistic information and (2) to create an intuitive user interface. The framework architecture has three layers: knowledge, uncertainty model and user interface. The contributions of the domain experts are decoupled, the ontology builder will create the domain concepts and relationships focusing on the domain knowledge only. The uncertainty model is Bayesian Network and the probabilities of the variables states are stored in a profile repository. The diagnostician will use the user interface feeded with the domain ontology and one uncertainty profile. The application was tested on a sample medicine model for the diagnose of heart disease. [Copyright &y& Elsevier]

Individuals with different backgrounds such as engineering, medicine, industrial design, business, healthcare management and computer science often contribute to the design of a medical innovation. But how do such heterogeneous design participants actually combine their expertise to develop a medical device? Adapting Bucciarelli’s concept of “object worlds”, which recognises that those who contribute to a design process inhabit different worlds and see the object of design differently, this paper examines the perspectives of 8 design participants who contributed to the design process of three Canadian medical devices. In-depth analyses of semi-structured interviews clarified what design participants saw through their particular “lens”, how their responsibilities, knowledge and motivations combined and how they engaged into the design process. [ABSTRACT FROM AUTHOR]

Abstract: Data Envelopment Analysis (DEA) is one of the best-known efficiency evaluation methods due to its advantages in selection of weights. Many research papers have extensively discussed the issue of weight restrictions, rather than those implied in the model itself. However, this often leads to a failure to represent the relations of certain weights, as well as underestimation of the efficiency of Decision Making Units (DMUs). When analyzing the medical sectors of Taiwan with the developed models and CCR, it is found that efficiency underestimation by efficient DMUs is more serious than that of inefficient DMUs. In addition, underestimation occurs when weights are concentrated in the same output, however, every output of referenced DMU is the same times of corresponding output of targeted DMU. [ABSTRACT FROM AUTHOR]

Abstract: Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering. [ABSTRACT FROM AUTHOR]

Abstract: Held on 21st to 23rd November 2009 in Hiroshima, the SiHIS working conference aimed at finding solutions to approach to an idealistic society where (1) the individual can trust information with full understanding and responsibility, (2) the individual can allow the use of information backed by sound legitimated environment, (3) information can play its role for better healthcare and the improvement of medicine. The purpose of this paper is to propose recommendations from this working conference. [ABSTRACT FROM AUTHOR]

Abstract: In decision-making problems under uncertainty, a decision table consists of a set of attributes indicating what is the optimal decision (response) within the different scenarios defined by the attributes. We recently introduced a method to give explanations of these responses. In this paper, the method is extended. To do this, it is combined with a query system to answer expert questions about the preferred action for a given instantiation of decision table attributes. The main difficulty is to accurately answer queries associated with incomplete instantiations. Incomplete instantiations are the result of the evaluation of a partial model outputting decision tables that only include a subset of the whole problem, leading to uncertain responses. Our proposal establishes an automatic and interactive dialogue between the decision-support system and the expert to elicit information from the expert to reduce uncertainty. Typically, the process involves learning a Bayesian network structure from a relevant part of the decision table and computing some interesting conditional probabilities that are revised accordingly. [ABSTRACT FROM AUTHOR]