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The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

Medical encounters are interactional/interpersonal processes taking place within contexts shaped by macro-level social structures. In the case of sexually transmitted diseases (STDs), medical encounters occur at a stigmatized crossroads of social control and gendered norms of sexual behavior. When women are diagnosed and treated for chronic STDs, practitioner demeanor has an important impact on how patients will view not only their health status but also their moral status. This chapter draws on in depth interviews with 40 women diagnosed with genital infections of herpes and/or human papillomavirus (HPV – the cause of genital warts) to explore three models of patient–practitioner interaction. The analysis focuses on the relationship between gender, construction of illness, and practitioner interaction style. In a broader context, the health risks posed by particular interaction styles to female STD patients shed light on larger public health implications of combining morality with medicine for the broader range of patients with stigmatizing diagnoses.

I want to read the controversies and scandals surrounding Institutional Review Boards (IRBs) within a critical pedagogical, discourse. Ethics are pedagogies of practice. IRBs are institutional apparatuses that regulate a particular form of ethical conduct, a form that may be no longer workable in a transdisciplinary, global, and postcolonial world. I seek a progressive performative cultural politics that enacts a performance ethics based on feminist, communitarian assumptions. I will attempt to align these assumptions with the call by First and Fourth World scholars for an indigenous research ethic (Smith, 1999; Bishop, 1998; Rains, Archibald, & Deyhle, 2000). This allows me to criticize the dominant biomedical and ethical model that operates in many North American universities today. I conclude with a preliminary outline of an indigenous, feminist, communitarian research ethic. This ethic has two implications. It would replace the current utilitarian ethical model that IRBs utilize. It argues for a two-track, or three-track IRB model within the contemporary university setting.

This chapter explores the social production of disablement, or disability as a process, and the effect of institutionalized administrative definitions of disability.

This research is based on 12 in-depth interviews with male construction workers in the southeastern United States.

The intersections of age, gender, and class are implicated in the production of occupational disablement. In addition, the power of definition residing with administrative entities plays an important role in how workers come to understand disability and disablement, ultimately affecting their ability to be self-advocates. This study also suggests that current conceptualizations of disability are not adequate for these participants whose experiences of disablement highlight its processual nature.

“Becoming” disabled, constricted activities outside of those defined by Activities of Daily Living (ADLs) or Instrumental Activities of Daily Living (IADLs), and activity limitation due to significant pain and discomfort are all issues that should be addressed in disability conceptualization.

The aim of this paper is to present and evaluate a methodology for automatically constructing and applying the physiologically‐realistic boundary/loading conditions for use in the structural finite element analysis of the femur during various exertion tasks (e.g. gait/walking).

To obtain physiologically‐realistic boundary/loading conditions needed in the femur structural finite element analysis, a whole‐body musculoskeletal inverse dynamics analysis is carried out and the resulting muscle forces and joint reaction forces/moments extracted.

The finite element results obtained are compared with their counterparts available in literature and it is found that the overall agreement is acceptable while the highly automated procedure for the finite element model generation developed in the present work made the analysis fairly easy and computationally highly efficient. Potential sources of errors in the current procedure have been identified and the measures for their mitigation recommended.

The present approach enables a more accurate determination of the physiological loads experienced by the orthopedic implants which can be of great value to implant designers and orthopedic surgeons.

This paper aims to provide an analysis of the mental health social work role, its contribution to social inclusion, and its ability to translate this into practice.

The paper considers national policy, research and theory to consider the nature of social work and the mental health system.

While social work is ideally placed to challenge the biomedical model and promote social inclusion, organisational and other failings would appear to seriously undermine its ability to do so.

The paper considers some important issues facing social work and mental health, and raises points for thought and discussion.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

The traditional role of librarians, securing and providing access to the archives of recorded knowledge, is being rapidly challenged on three fronts: the increasing technical sophistication of library users, the wide availability of information management software, and economic pressures on the growth of library collections. While we may argue endlessly about ‘turf’ and ‘ownership’ the real challenge is to forge a new ethic oflibrarianship which defies the stereotype of the passive provider of information services, explores new models of partnership with users and champions an interactive environment in which to create the library of the future. The Main Library at Lawrence Livermore National Laboratory is actively engaged in testing experimental models of the library of the future centred on dynamic user‐librarian interactions, versatile information technologies and on fostering creative partnerships with library users in different environments. These experiments are described and evaluated, and their implications for future development are explored.