Evolving large scale healthcare applications using open standards

2017 ◽  
Vol 6 (4) ◽  
pp. 410-425 ◽  
Author(s):  
Shelly Sachdeva ◽  
Shivani Batra ◽  
Subhash Bhalla
Keyword(s):  
Large Scale ◽  
Healthcare Applications ◽  
Open Standards

scholarly journals Medimate : Ailment Diffusion Control System with Real Time Large Scale Data Processing

2018 ◽  
Vol 7 (2.31) ◽  
pp. 240
Author(s):  
S Sujeetha ◽  
Veneesa Ja ◽  
K Vinitha ◽  
R Suvedha
Keyword(s):  
Control System ◽  
Data Processing ◽  
Real Time ◽  
Large Scale ◽  
Diffusion Control ◽  
Qr Code ◽  
Healthcare Applications ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

In the existing scenario, a patient has to go to the hospital to take necessary tests, consult a doctor and buy prescribed medicines or use specified healthcare applications. Hence time is wasted at hospitals and in medical shops. In the case of healthcare applications, face to face interaction with the doctor is not available. The downside of the existing scenario can be improved by the Medimate: Ailment diffusion control system with real time large scale data processing. The purpose of medimate is to establish a Tele Conference Medical System that can be used in remote areas. The medimate is configured for better diagnosis and medical treatment for the rural people. The system is installed with Heart Beat Sensor, Temperature Sensor, Ultrasonic Sensor and Load Cell to monitor the patient’s health parameters. The voice instructions are updated for easier access.  The application for enabling video and voice communication with the doctor through Camera and Headphone is installed at both the ends. The doctor examines the patient and prescribes themedicines. The medical dispenser delivers medicine to the patient as per the prescription. The QR code will be generated for each prescription by medimate and that QR code can be used forthe repeated medical conditions in the future. Medical details are updated in the server periodically.  

The Pransky interview: Dr Cory Kidd, Founder and CEO at Catalia Health

2017 ◽  
Vol 44 (3) ◽  
pp. 259-263 ◽  
Author(s):  
Joanne Pransky
Keyword(s):  
Large Scale ◽  
Information Science ◽  
Industrial Robot ◽  
Human Robot Interaction ◽  
High Tech ◽  
Healthcare Applications ◽  
Robot Interaction ◽  
Wall Street ◽  
Content Type ◽  
Institute Of Technology

Purpose The purpose of this paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned-entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach The interviewee is Dr Cory Kidd, an inventor, entrepreneur and leading practitioner in the field of human–robot interaction. Dr Kidd shares his 20-year journey of working at the intersection of healthcare and technology and how he applied innovative technologies toward solving large-scale consumer healthcare challenges. Findings Dr Kidd received his BS degree in Computer Science from the Georgia Institute of Technology and earned a National Science Foundation Graduate Research Fellow in Computer and Information Science & Engineering. Dr Kidd received his MS and PhD degrees at the MIT Media Lab in human–robot interaction. While there, he conducted studies that showed the psychological and clinical advantages of using a physical robot over screen-based interactions. While finishing his PhD in 2007, he founded his first company, Intuitive Automata, which created interactive coaches for weight loss. Though Intuitive Automata ceased operations in 2013, Dr Kidd harnessed his extensive knowledge of the healthcare business and the experiences from patient engagement and launched Catalia Health in 2014 with a new platform centered specifically around patient behavior change programs for chronic disease management. Originality/value Dr Kidd is a pioneer of social robotics and has developed groundbreaking technology for healthcare applications that combines artificial intelligence, psychology and medical best practices to deliver everyday care to patients who are managing chronic conditions. He holds patents, including one entitled Apparatus and Method for Assisting in Achieving Desired Behavior Patterns and in an Interactive Personal Health Promoting Robot. Dr Kidd was awarded the inaugural Wall Street Journal and Credit Suisse Technopreneur of the Year in 2010, which is meant to “honor the entry that best applies technology with the greatest potential for commercial success”. He is also the Director of Business Development for the nonprofit Silicon Valley Robotics and is an impact partner for Fresco Capital. He consults, mentors and serves as a Board Member and Advisor to several high-tech startups.

Open Cloud Technologies

2011 ◽  
pp. 1-17 ◽  
Author(s):  
Andres-Leonardo Martinez-Ortiz
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Technological Development ◽  
Cloud Systems ◽  
Interesting Insight ◽  
New Strategy ◽  
Pros And Cons ◽  
Open Standards ◽  
Cloud Technologies ◽  
Current Systems

The open source perspective offers an interesting insight about cloud computing technologies: in one hand, cloud systems belong to the category of the Ultra-Large-Scale (ULS) systems, i.e. very complex systems where conventional approach for the technological development does not work. For such as systems, Free Libre Open Source Software (FLOSS) licensing attracts innovation from the developers’ communities, reduces the risks of technology adoption and fosters the interoperability between systems and the creation of open standards. In the other hand, the current systems are far from achieving interoperability; even the FLOSS´s principles remain pending for many components in the architecture of the main cloud solutions, and for these reasons many FLOSS evangelists do not recommend using them. As a balance between the obvious drawbacks and benefits, recently a new strategy has appeared: Free/Open Services. However, it seems difficult to find short term solutions. This chapter illustrates both ideas, highlighting the pros and cons of these technologies, including a reference of main “open cloud” groups and open source technologies for the cloud. The rest of the book will include additional and deeper descriptions of some of the most interesting open cloud technologies.

Scheduling with Deadline Constraint of Healthcare Applications on Cloud-Based Workflow

2020 ◽  
Vol 10 (10) ◽  
pp. 2430-2438
Author(s):  
Haiying Che ◽  
Xiaolong Wang ◽  
Hong Wang ◽  
Zixing Bai ◽  
Honglei Li
Keyword(s):  
Large Scale ◽  
Ant Colony Algorithm ◽  
Scheduling Algorithm ◽  
Healthcare Applications ◽  
Start Time ◽  
Workflow Technology ◽  
Factors Affecting ◽  
Heuristic Scheduling ◽  
Tasks Scheduling ◽  
High Flexibility

Cloud-based workflow technology has played an important role in the development of large scale healthcare applications with high flexibility to meet variety of healthcare process requirements. Among all the factors affecting the healthcare applications on cloud-based workflow, the tasks scheduling is the crucial one. This paper aims at the cloud-based workflow tasks scheduling with deadline constraints and its implementation in two approaches: heuristic scheduling algorithm (HSA) and meta heuristic scheduling algorithm (HSA-ACO). HSA decomposes the workflow according to its structure and divide the deadline into the level deadlines. Tasks in each level get scheduling priority according to the earliest start time under the constraint of level deadline. In another method, HSA-ACO integrates HSA with ant colony algorithm to achieve better performance. In the last part, we launch the experiment to compare HSA and HSA-ACO with algorithms like Prolis, LACO and ICPCP in three types of workflow with different scales. The experiment results show that HSA-ACO is better than the other algorithms.

scholarly journals Systematic Understanding of Recent Developments in Bacterial Cellulose Biosynthesis at Genetic, Bioprocess and Product Levels

2021 ◽  
Vol 22 (13) ◽  
pp. 7192
Author(s):  
Gizem Buldum ◽  
Athanasios Mantalaris
Keyword(s):  
Bacterial Cellulose ◽  
Large Scale ◽  
Product Yield ◽  
High Water ◽  
Scale Production ◽  
Healthcare Applications ◽  
Model Driven ◽  
Recent Developments ◽  
Increasing Demand ◽  
Systematic Understanding

Engineering biological processes has become a standard approach to produce various commercially valuable chemicals, therapeutics, and biomaterials. Among these products, bacterial cellulose represents major advances to biomedical and healthcare applications. In comparison to properties of plant cellulose, bacterial cellulose (BC) shows distinctive characteristics such as a high purity, high water retention, and biocompatibility. However, low product yield and extensive cultivation times have been the main challenges in the large-scale production of BC. For decades, studies focused on optimization of cellulose production through modification of culturing strategies and conditions. With an increasing demand for BC, researchers are now exploring to improve BC production and functionality at different categories: genetic, bioprocess, and product levels as well as model driven approaches targeting each of these categories. This comprehensive review discusses the progress in BC platforms categorizing the most recent advancements under different research focuses and provides systematic understanding of the progress in BC biosynthesis. The aim of this review is to present the potential of ‘modern genetic engineering tools’ and ‘model-driven approaches’ on improving the yield of BC, altering the properties, and adding new functionality. We also provide insights for the future perspectives and potential approaches to promote BC use in biomedical applications.

Developing Relationship Among Risk Factors and Project Factors for Large Scale Healthcare Applications

2020 ◽  
Vol 10 (10) ◽  
pp. 2439-2445
Author(s):  
Sabeen Tahir ◽  
Basit Shahzad ◽  
Sheikh Tahir Bakhsh ◽  
Mohammed Basheri
Keyword(s):  
Risk Factors ◽  
Resource Allocation ◽  
Large Scale ◽  
Effective Control ◽  
Mixed Method Research ◽  
Application Development ◽  
Healthcare Applications ◽  
Damage Level ◽  
Software Risk ◽  
The Impact

The impact and probability of healthcare software risks are widely discussed in software risk management. The identification of healthcare software risk factor will not be useful without measuring its impact and probability. The impact filed determines the damage level of the risk factors if it appears, while the probability determines how probable is the existence of that risk. The risks of having high probability and higher impact levels have to be addressed urgently. The orientation of the risks takes place in response to unbalanced resource allocation. Establishing an association among the project factors and risk factors helps in gaining more effective control of the resource allocation to the healthcare application development. This paper identifies the conceptual and numerical association between the project factors and risk factors by conducting a mixed-method research analysis to optimize the resource allocation and reduce (eliminate) the software risks in large scale healthcare applications. The resultant associations between the project factors and risk factors are categorized as weak or strong based on the resultant data of this study and can be used as a guideline document for developing the large scale healthcare systems.

Developing Relationship Among Risk Factors and Project Factors for Large Scale Healthcare Applications

2020 ◽  
Vol 10 (10) ◽  
pp. 2439-2445
Author(s):  
Sabeen Tahir ◽  
Basit Shahzad ◽  
Sheikh Tahir Bakhsh ◽  
Mohammed Basheri
Keyword(s):  
Risk Factors ◽  
Resource Allocation ◽  
Large Scale ◽  
Effective Control ◽  
Mixed Method Research ◽  
Application Development ◽  
Healthcare Applications ◽  
Damage Level ◽  
Software Risk ◽  
The Impact

The impact and probability of healthcare software risks are widely discussed in software risk management. The identification of healthcare software risk factor will not be useful without measuring its impact and probability. The impact filed determines the damage level of the risk factors if it appears, while the probability determines how probable is the existence of that risk. The risks of having high probability and higher impact levels have to be addressed urgently. The orientation of the risks takes place in response to unbalanced resource allocation. Establishing an association among the project factors and risk factors helps in gaining more effective control of the resource allocation to the healthcare application development. This paper identifies the conceptual and numerical association between the project factors and risk factors by conducting a mixed-method research analysis to optimize the resource allocation and reduce (eliminate) the software risks in large scale healthcare applications. The resultant associations between the project factors and risk factors are categorized as weak or strong based on the resultant data of this study and can be used as a guideline document for developing the large scale healthcare systems.

scholarly journals Reusable and Usable Environment for the Digital Courseware Domain

10.28945/2461 ◽  
2002 ◽  
Author(s):  
Elsabe Cloete ◽  
Paula Kotze
Keyword(s):  
Software Architecture ◽  
Large Scale ◽  
Course Design ◽  
Authoring Tool ◽  
Software Systems ◽  
Interactive Software ◽  
Reusable Components ◽  
Open Standards ◽  
The Creation ◽  
Usability Requirements

This paper considers a functional framework that creates a usable authoring support environment (ASE) for digital course design, and outputs reusable components. Within the context of considering the courseware domain as a domain of interactive software systems, we developed an ASE prototype. The objectives of this prototype include the provision of a usable authoring tool to develop interactive courseware, as well as the creation of domain products that are based on open standards to foster large-scale reuse of these products. In this paper we describe the software architecture of the prototype, based on usability requirements.

scholarly journals MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

2021 ◽  
Author(s):  
Chuizheng Meng ◽  
Loc Trinh ◽  
Nan Xu ◽  
Yan Liu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Medical Information ◽  
Prediction Models ◽  
Short Term Memory ◽  
Mortality Prediction ◽  
Learning Models ◽  
Healthcare Applications ◽  
Disparate Treatment ◽  
Mortality Predictors

Abstract The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of dataset representation bias as well as interpretability and prediction fairness of deep learning models for in-hospital mortality prediction. In terms of interpretability, we observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models; (2) demographic features are important for prediction. In terms of fairness, we observe that (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) all of the studied mortality predictors are generally fair while the IMV-LSTM (Interpretable Multi-Variable Long Short-Term Memory) model provides the most accurate and unbiased predictions across all protected groups. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors.

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