scholarly journals Pathological Test Type and Chemical Detection Using Deep Neural Networks: A Case Study Using ELISA and LFA Assays

2020 ◽  
Author(s):  
Marzia Hoque Tania ◽  
M. Shamim Kaiser ◽  
Kamal J. Abu-Hassan ◽  
M A Hossain
Keyword(s):  
Intelligent Systems ◽  
Design Methodology ◽  
Deep Neural Networks ◽  
Healthcare Professionals ◽  
Gradual Increase ◽  
Visual Knowledge ◽  
High Degree ◽  
Type Classification ◽  
Assay Detection

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marzia Hoque Tania ◽  
M. Shamim Kaiser ◽  
Kamal Abu-Hassan ◽  
M. A. Hossain
Keyword(s):  
Intelligent Systems ◽  
Design Methodology ◽  
Deep Neural Networks ◽  
Healthcare Professionals ◽  
Content Type ◽  
Visual Knowledge ◽  
High Degree ◽  
Type Classification ◽  
Assay Detection

PurposeThe gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on.Design/methodology/approachThe concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results.FindingsThis paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time.Originality/valueTo the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

scholarly journals Providing healthcare through “value shops”: impact on professional fulfilment for physicians and nurses

2020 ◽  
Vol 25 (2) ◽  
pp. 127-136
Author(s):  
Christian Gadolin ◽  
Thomas Andersson ◽  
Erik Eriksson ◽  
Andreas Hellström
Keyword(s):  
Design Methodology ◽  
Healthcare Professionals ◽  
Qualitative Case Study ◽  
Quality Care ◽  
Healthcare Setting ◽  
Positive Outcomes ◽  
Care Provision ◽  
Quality Improvements ◽  
Content Type

PurposeThe purpose of this paper is to empirically explore and demonstrate the ability of healthcare professionals to attain professional fulfilment when providing healthcare inspired by “value shops”.Design/methodology/approachA qualitative case study incorporating interviews and observations was conducted.FindingsThe empirical data suggest that the professional fulfilment of both physicians and nurses is facilitated when care is organized through “value shops”. Both groups of professionals state that they are able to return to their “professional core”.Originality/valueThe beneficial outcomes of organizing healthcare inspired by the “value shop” have previously been explored in terms of efficiency and quality. However, the professional fulfilment of healthcare professionals when providing such care has not been explicitly addressed. Professional fulfilment is vital in order to safeguard high-quality care, as well as healthcare professionals' involvement and engagement in implementing quality improvements. This paper highlights the fact that care provision inspired by the “value shop” may facilitate professional fulfilment, which further strengthens the potential positive outcomes of the “value shop” when utilized in a healthcare setting.

A value-based taxonomy of improvement approaches in healthcare

2017 ◽  
Vol 31 (4) ◽  
pp. 445-458 ◽  
Author(s):  
Christian Colldén ◽  
Ida Gremyr ◽  
Andreas Hellström ◽  
Daniella Sporraeus
Keyword(s):  
Design Methodology ◽  
Healthcare Professionals ◽  
Management Practice ◽  
Healthcare Management ◽  
Patient Centered ◽  
Content Type ◽  
A Value ◽  
Centered Care ◽  
Organizational Focus

Purpose The concept of value is becoming increasingly fashionable in healthcare and various improvement approaches (IAs) have been introduced with the aim of increasing value. The purpose of this paper is to construct a taxonomy that supports the management of parallel IAs in healthcare. Design/methodology/approach Based on previous research, this paper proposes a taxonomy that includes the dimensions of view on value and organizational focus; three contemporary IAs – lean, value-based healthcare, and patient-centered care – are related to the taxonomy. An illustrative qualitative case study in the context of psychiatric (psychosis) care is then presented that contains data from 23 interviews and focuses on the value concept, IAs, and the proposed taxonomy. Findings Respondents recognized the dimensions of the proposed taxonomy and indicated its usefulness as support for choosing and combining different IAs into a coherent management model, and for facilitating dialog about IAs. The findings also suggested that the view of value as “health outcomes” is widespread, but healthcare professionals are less likely than managers to also view value as a process. Originality/value The conceptual contribution of this paper is to delineate some important characteristics of IAs in relation to the emerging “value era”. It also highlights the coexistence of different IAs in healthcare management practice. A taxonomy is proposed that can help managers choose, adapt, and combine IAs in local management models.

scholarly journals APPLICATION OF DOMAIN INTEGRATED DESIGN METHODOLOGY FOR BIO-INSPIRED DESIGN- A CASE STUDY OF SUTURE PIN DESIGN

2021 ◽  
Vol 1 ◽  
pp. 487-496
Author(s):  
Pavan Tejaswi Velivela ◽  
Nikita Letov ◽  
Yuan Liu ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Cross Sections ◽  
Design Methodology ◽  
Reaction Force ◽  
Integrated Design ◽  
Total Deformation ◽  
Insertion Force ◽  
Force Reaction ◽  
The Moment ◽  
Work Done

AbstractThis paper investigates the design and development of bio-inspired suture pins that would reduce the insertion force and thereby reducing the pain in the patients. Inspired by kingfisher's beak and porcupine quills, the conceptual design of the suture pin is developed by using a unique ideation methodology that is proposed in this research. The methodology is named as Domain Integrated Design, which involves in classifying bio-inspired structures into various domains. There is little work done on such bio-inspired multifunctional aspect. In this research we have categorized the vast biological functionalities into domains namely, cellular structures, shapes, cross-sections, and surfaces. Multi-functional bio-inspired structures are designed by combining different domains. In this research, the hypothesis is verified by simulating the total deformation of tissue and the needle at the moment of puncture. The results show that the bio-inspired suture pin has a low deformation on the tissue at higher velocities at the puncture point and low deformation in its own structure when an axial force (reaction force) is applied to its tip. This makes the design stiff and thus require less force of insertion.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

Towards a taxonomy of intergenerational knowledge transfer practices

2018 ◽  
Vol 25 (2) ◽  
pp. 81-91
Author(s):  
Kerstin Kuyken ◽  
Mehran Ebrahimi ◽  
Anne-Laure Saives
Keyword(s):  
Knowledge Transfer ◽  
Comparative Study ◽  
Design Methodology ◽  
Qualitative Case Study ◽  
High Tech ◽  
Content Type ◽  
Case Study Design ◽  
Younger Workers ◽  
Intergenerational Knowledge

Purpose This paper aims to develop a better understanding of intergenerational knowledge transfer (IKT) practices by adopting a context-related and comparative perspective. Design/methodology/approach A qualitative case study design involving 83 interviews and non-participative observation in German and Quebec organizations has been chosen. Findings Two distinctive archetypes of IKT emerge from both national contexts: “we-individualizing” (Germany) and “I-connecting” (Quebec), leading to an eightfold taxonomy of IKT practices. Research limitations/implications This research is limited to young and senior workers and to high-tech sectors. Originality/value Comparative and inductive study of IKT, adaptation of IKT practices to national contexts, retaining younger workers. This inductive and comparative study allows a better adaptation of IKT practices to national contexts and therefore a better retention of younger workers.

CSim 2

2021 ◽  
Vol 43 (1) ◽  
pp. 1-46
Author(s):  
David Sanan ◽  
Yongwang Zhao ◽  
Shang-Wei Lin ◽  
Liu Yang
Keyword(s):  
Programming Languages ◽  
Concurrent Systems ◽  
Theorem Prover ◽  
Compositional Techniques ◽  
Machine Code ◽  
Top Down ◽  
Hol Theorem Prover ◽  
High Level ◽  
High Degree

To make feasible and scalable the verification of large and complex concurrent systems, it is necessary the use of compositional techniques even at the highest abstraction layers. When focusing on the lowest software abstraction layers, such as the implementation or the machine code, the high level of detail of those layers makes the direct verification of properties very difficult and expensive. It is therefore essential to use techniques allowing to simplify the verification on these layers. One technique to tackle this challenge is top-down verification where by means of simulation properties verified on top layers (representing abstract specifications of a system) are propagated down to the lowest layers (that are an implementation of the top layers). There is no need to say that simulation of concurrent systems implies a greater level of complexity, and having compositional techniques to check simulation between layers is also desirable when seeking for both feasibility and scalability of the refinement verification. In this article, we present CSim 2 a (compositional) rely-guarantee-based framework for the top-down verification of complex concurrent systems in the Isabelle/HOL theorem prover. CSim 2 uses CSimpl, a language with a high degree of expressiveness designed for the specification of concurrent programs. Thanks to its expressibility, CSimpl is able to model many of the features found in real world programming languages like exceptions, assertions, and procedures. CSim 2 provides a framework for the verification of rely-guarantee properties to compositionally reason on CSimpl specifications. Focusing on top-down verification, CSim 2 provides a simulation-based framework for the preservation of CSimpl rely-guarantee properties from specifications to implementations. By using the simulation framework, properties proven on the top layers (abstract specifications) are compositionally propagated down to the lowest layers (source or machine code) in each concurrent component of the system. Finally, we show the usability of CSim 2 by running a case study over two CSimpl specifications of an Arinc-653 communication service. In this case study, we prove a complex property on a specification, and we use CSim 2 to preserve the property on lower abstraction layers.

scholarly journals A mixed methods multiple case study to evaluate the implementation of a care pathway for colorectal cancer surgery using extended normalization process theory

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
R. van Zelm ◽  
E. Coeckelberghs ◽  
W. Sermeus ◽  
A. Wolthuis ◽  
L. Bruyneel ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Healthcare Professionals ◽  
Care Pathway ◽  
Implementation Process ◽  
Colorectal Cancer Surgery ◽  
Protocol Adherence ◽  
Multiple Case ◽  
Specific Factors ◽  
Normalization Process

Abstract Background Specific factors that facilitate or prevent the implementation of enhanced recovery protocols for colorectal cancer surgery have been described in previous qualitative studies. This study aims to perform a concurrent qualitative and quantitative evaluation of factors associated with successful implementation of a care pathway (CP) for patients undergoing surgery for colorectal cancer. Methods This comparative mixed methods multiple case study was based on a sample of 10 hospitals in 4 European countries that implemented a specific CP and performed pre- and post-implementation measurements. In-depth post-implementation interviews were conducted with healthcare professionals who were directly involved. Primary outcomes included protocol adherence and improvement rate. Secondary outcomes included length of stay (LOS) and self-rated protocol adherence. The hospitals were ranked based on these quantitative findings, and those with the highest and lowest scores were included in this study. Qualitative data were summarized on a per-case basis using extended Normalization Process Theory (eNPT) as theoretical framework. The data were then combined and analyzed using joint display methodology. Results Data from 381 patients and 30 healthcare professionals were included. Mean protocol adherence rate increased from 56 to 62% and mean LOS decreased by 2.1 days. Both measures varied greatly between hospitals. The two highest-ranking hospitals and the three lowest-ranking hospitals were included as cases. Factors which could explain the differences in pre- and post-implementation performance included the degree to which the CP was integrated into daily practice, the level of experience and support for CP methodology provided to the improvement team, the intrinsic motivation of the team, shared goals and the degree of management support, alignment of CP development and hospital strategy, and participation of relevant disciplines, most notably, physicians. Conclusions Overall improvement was achieved but was highly variable among the 5 hospitals evaluated. Specific factors involved in the implementation process that may be contributing to these differences were conceptualized using eNPT. Multidisciplinary teams intending to implement a CP should invest in shared goals and teamwork and focus on integration of the CP into daily processes. Support from hospital management directed specifically at quality improvement including audit may likewise facilitate the implementation process. Trial registration NCT02965794. US National Library of Medicine, ClinicalTrials.gov. Registered 4 August 2014.

Digital forensics and evolving cyber law: case of BIMSTEC countries

2019 ◽  
Vol 22 (4) ◽  
pp. 744-752
Author(s):  
Sisira Dharmasri Jayasekara ◽  
Iroshini Abeysekara
Keyword(s):  
Information Technology ◽  
Design Methodology ◽  
Digital Forensics ◽  
Content Type ◽  
Global Context ◽  
Study Approach ◽  
Legal Frameworks ◽  
Work Done

Purpose The purpose of this paper is to discuss the role of digital forensics in an evolving environment of cyber laws giving attention to Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, comprising Bangladesh, India, Myanmar, Sri Lanka, Thailand, Nepal and Bhutan, in a dynamic global context. Design/methodology/approach This study uses a case study approach to discuss the digital forensics and cyber laws of BIMSTEC countries. The objective of the study was expected to be achieved by referring to decided cases in different jurisdictions. Cyber laws of BIMSTEC countries were studied for the purpose of this study. Findings The analysis revealed that BIMSTEC countries are required to amend legislation to support the growth of information technology. Most of the legislation are 10-15 years old and have not been amended to resolve issues on cyber jurisdictions. Research limitations/implications This study was limited to the members of the BIMSTEC. Originality/value This paper is an original work done by the authors who have discussed the issues of conducting investigations with respect to digital crimes in a rapidly changing environment of information technology and deficient legal frameworks.

scholarly journals Methods for conducting international Delphi surveys to optimise global participation in core outcome set development: a case study in gastric cancer informed by a comprehensive literature review

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bilal Alkhaffaf ◽  
Jane M. Blazeby ◽  
Aleksandra Metryka ◽  
Anne-Marie Glenny ◽  
Ademola Adeyeye ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Literature Review ◽  
Healthcare Professionals ◽  
Vascular Malformations ◽  
Methodological Approach ◽  
Core Outcome Set ◽  
Core Outcome ◽  
Comprehensive Literature Review ◽  
International Participation

Abstract Background Core outcome sets (COS) should be relevant to key stakeholders and widely applicable and usable. Ideally, they are developed for international use to allow optimal data synthesis from trials. Electronic Delphi surveys are commonly used to facilitate global participation; however, this has limitations. It is common for these surveys to be conducted in a single language potentially excluding those not fluent in that tongue. The aim of this study is to summarise current approaches for optimising international participation in Delphi studies and make recommendations for future practice. Methods A comprehensive literature review of current approaches to translating Delphi surveys for COS development was undertaken. A standardised methodology adapted from international guidance derived from 12 major sets of translation guidelines in the field of outcome reporting was developed. As a case study, this was applied to a COS project for surgical trials in gastric cancer to translate a Delphi survey into 7 target languages from regions active in gastric cancer research. Results Three hundred thirty-two abstracts were screened and four studies addressing COS development in rheumatoid and osteoarthritis, vascular malformations and polypharmacy were eligible for inclusion. There was wide variation in methodological approaches to translation, including the number of forward translations, the inclusion of back translation, the employment of cognitive debriefing and how discrepancies and disagreements were handled. Important considerations were identified during the development of the gastric cancer survey including establishing translation groups, timelines, understanding financial implications, strategies to maximise recruitment and regulatory approvals. The methodological approach to translating the Delphi surveys was easily reproducible by local collaborators and resulted in an additional 637 participants to the 315 recruited to complete the source language survey. Ninety-nine per cent of patients and 97% of healthcare professionals from non-English-speaking regions used translated surveys. Conclusion Consideration of the issues described will improve planning by other COS developers and can be used to widen international participation from both patients and healthcare professionals.

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