How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. (Preprint)

2021 ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Lymphocytic Leukemia ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

BACKGROUND Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have actually been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions to increase the likelihood of successful implementation, but recommendations about their use are lacking. OBJECTIVE The aim of this study was to develop and apply a framework that positions best-practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. METHODS The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Hereafter, economic evaluations were used to determine critical thresholds and guide investment decisions. RESULTS Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs resulted in an unfavorable cost-effectiveness ratio for potential users. In the intensive care, analytics reduced mortality and per-patient costs when used to identify infections (-0.5%, -€886) and also to improve patient-ventilator interaction (-3%, -€264). Both analytics hold the potential to save money but the return on investment for developers of analytics that identify infections strongly depends on infection rate; a higher rate implies greater cost-savings. CONCLUSIONS We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

scholarly journals How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

scholarly journals The Use of Technical Analysis in the US, European and Asian Stock Markets

2020 ◽  
Vol 8 ◽  
pp. 302-318
Author(s):  
Deimante Teresiene ◽  
Margarita Aleksynaite
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Stock Markets ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Big Data Analysis ◽  
The Us

Technical analysis is a widely used tool in making investment decisions. Nowadays it becomes very popular in the context of big data analysis and artificial intelligence framework. Although the analysis of the results of indicators in certain markets often becomes the axis of technical analysis research, it is difficult to find articles aimed at applying and comparing this analysis in different markets. This paper attempts to answer the question of whether technical analysis indicators work in the same or different ways in the US, European, and Asian stock markets. For this purpose, 8 indicators are calculated, and their results are compared in three selected markets. The correlation between the indicators themselves in individual markets is also determined. It has been observed that the performance of technical analysis is similar in different markets so this type of analysis can be used in artificial intelligence framework.

scholarly journals Simulation of energy consumption in urban areas

2020 ◽  
Vol 152 ◽  
pp. 02006
Author(s):  
Nikolay Garyaev
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Energy Consumption ◽  
Urban Areas ◽  
Energy Supply ◽  
Digital Data ◽  
Successful Implementation ◽  
Digital Models ◽  
Balanced Distribution ◽  
The Way

One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.

scholarly journals A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence

2021 ◽  
pp. 177-210
Author(s):  
Edward Curry ◽  
Edo Osagie ◽  
Niki Pavlopoulou ◽  
Dhaval Salwala ◽  
Adegboyega Ojo
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Best Practice ◽  
Success Factors ◽  
Critical Success ◽  
Operational Capabilities ◽  
And Performance ◽  
Practice Framework ◽  
Centres Of Excellence

AbstractThis chapter presents a best practice framework for the operation of Big Data and Artificial Intelligence Centres of Excellence (BDAI CoE). The goal of the framework is to foster collaboration and share best practices among existing centres and support the establishment of new Centres of Excellence (CoEs) within Europe. The framework was developed following a phased design science process, starting from a literature review to create an initial framework which was enhanced with the findings of a multi-case study of existing successful CoEs. Each case study involved an in-depth analysis and a series of in-depth interviews with leadership personnel of existing CoEs.The resulting best practice framework models a CoE using open systems theory that comprises input (environment), transformation (CoE) and output (impact). The framework conceptualises the internal operation of the CoE as a set of high-level capabilities including strategy, governance, structure, funding, and people and culture. The core capabilities of the CoE include business development, collaboration, research support services, technical infrastructure, experimentation/demonstration platforms, Intellectual Property (IP) and data protection, education and public engagement, policy outreach, technology and knowledge transfer, and performance and impact assessment. In this chapter we describe the best practice framework for CoEs in big data and AI, including objectives, environment, strategic and operational capabilities, and impact. The chapter outlines how the framework can be used by a CoE to support its strategic direction and operational decisions over time, and how a new CoE can use it in the start-up phase. Based on the analysis of the case studies, the chapter explores the critical success factors of a CoE as defined by a survey of CoE managers. Finally, the chapter concludes with a summary.

scholarly journals Artificial intelligence: what should an intensivist have in mind in the beginning of the new era?

2021 ◽  
Vol 25 (1) ◽  
pp. 8-12
Author(s):  
Luiz Alberto Cerqueira Batista Filho
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Critical Care ◽  
Intensive Care ◽  
Black Box ◽  
Care Physician ◽  
New Era ◽  
Intensive Care Physician ◽  
The Subject ◽  
In The Beginning

A new era is coming for medicine, and for critical care in particular. The intensive care unit is at the edge of being completely changed by artificial intelligence, and many challenges are ahead of the intensive care physician. This article aims to address the benefits and difficulties that big data will bring to clinicians, and to provide an overview on the subject. Key words: Big Data; Artificial intelligence; ICU; Critical Care; Black box Citation: Filho LACB. Artificial intelligence: what should an intensivist have in mind in the beginning of the new era. Anaesth. Pain intensive care 2021;25(1):8-12. DOI: 10.35975/apic.v25i1.1428 Received: 10 December 2020, Reviewed: 3 January 2021, Accepted: 8 January 2021

Digital health solutions and state of interoperability: Sierra Leone’s Landscape analysis (Preprint)

2021 ◽  
Author(s):  
Emeka Chukwu ◽  
Edward Foday ◽  
Abdul Konomanyi ◽  
Royston Wright ◽  
Lalit Garg ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Sierra Leone ◽  
Health Management ◽  
Digital Health ◽  
Health Information System ◽  
Implementation Strategies ◽  
Health Facilities ◽  
Successful Implementation ◽  
District Health

BACKGROUND Government and partners have invested heavily in the health information system (HIS) for service delivery, surveillance, reporting, and monitoring. Sierra Leone government launched her first digital health strategy in 2018. In 2019, a broader National Innovation and digital strategy was launched. The health-pillar direction will use Big data and Artificial Intelligence (AI) to improve healthcare in general, and maternal and child health in particular. Understanding the number, distribution, and interoperability of digital health solutions is crucial for successful implementation strategies. OBJECTIVE This paper presents the state of digital health solutions in Sierra Leone, and how these solutions currently interoperate. This study further presents opportunities for big data and AI application. METHODS All the district health management teams, Digital health implementing organizations, and sample Seventy-two health facilities representatives were surveyed. RESULTS Health facility survey shows that 94% of health facilities had at least one digital health project being implemented. The National Health Management Information (NHMIS) aggregate reporting solution was by far the most used. Half of health facilities had more than two digital health solutions in use. Data was not being exchanged among the surveyed digital health systems. CONCLUSIONS The different digital health software solutions do not share data amongst one another, though reporting data is sent as necessary. The vision of using big data for healthcare is achievable if stakeholders prioritize these healthcare exchange using agreed use cases from the national strategies. Many digital health solutions are currently used at health facilities in Sierra Leone. Government can leverage current investment in HIS from surveillance and reporting for using big data and artificial intelligence for care. This study has shown evidence of distribution, types, and scale of digital health solutions in health facilities, and opportunities for leveraging big data to fill critical gap necessary to achieve the national digital health vision.

A Study on the Development of Appointment Algorithm of CT With Big Data using AI (Artificial Intelligence)

2018 ◽  
Vol 20 (2) ◽  
pp. 1-5
Author(s):  
Sang-ho Jeon ◽  
Sung-yeul Yang ◽  
In-beom Shin ◽  
Dae-mok Son ◽  
Tae-han Kwon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

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