An Experimental Healthcare System

2021 ◽  
pp. 301-308
Author(s):  
Miroslav M. Bojović ◽  
Veljko Milutinović ◽  
Dragan Bojić ◽  
Nenad Korolija
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Knowledge Management ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Clinical Guidelines ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Starting Point ◽  
Data Architecture

Contemporary healthcare systems face growing demand for their services, rising costs, and a workforce. Artificial intelligence has the potential to transform how care is delivered and to help meet the challenges. Recent healthcare systems have been focused on using knowledge management and AI. The proposed solution is to reach explainable and causal AI by combining the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion in a way that is accessible and understandable to physicians. Therefore, the authors propose AI approach in which the encoded clinical guidelines and protocols provide a starting point augmented by models that learn from data. The new structure of electronic health records that connects data from wearables and genomics data and innovative extensible big data architecture appropriate for this AI concept is proposed. Consequently, the proposed technology may drastically decrease the need for expensive software and hopefully eliminates the need to do diagnostics in expensive institutions.

Artificial Intelligence and Machine Learning Algorithms

2020 ◽  
pp. 188-219
Author(s):  
Amit Kumar Tyagi ◽  
Poonam Chahal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Artificial Intelligence Sebagai Penggerak Industri 4.0 dan Tantangannya Bagi Sektor Pemerintah dan Swasta

2019 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Reza Yogaswara
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Competitive Advantage ◽  
Smart City ◽  
Chief Information Officer ◽  
Deep Learning Neural Network

Artificial Intelligence (AI) atau kecerdasan buatan menjadi penggerak revolusi industri 4.0 yang menjanjikan banyak kemudahan bagi sektor pemerintah maupun industri. Internet of Things (IoT) dan big data contohnya dimana AI dapat diimplementasikan, teknologi yang telah banyak diadopsi di era industri 4.0 ini mampu menghubungkan setiap perangkat, seseorang dapat mengotomatisasi semua perangkat tanpa harus berada di lokasi, lebih dari itu, saat ini telah banyak mesin yang dapat menginterprestasi suatu kondisi atau kejadian tertentu dengan bantuan AI, sebagaimana telah kamera cerdas pendeteksi kepadatan volume kendaraan di jalan raya menggunakan teknologi Deep Learning Neural Network, yang telah diimplementasikan pada beberapa Pemerintah Daerah Kabupaten dan Kota dalam mendukung program Smart City yang telah dicanangkan. Pada sektor industri, banyak juga dari mereka yang telah mengotomatisasi mesin produksi dan manufaktur menggunakan robot dan Artificial Intelligence, sehingga Industri 4.0 akan meningkatkan daya saing melalui perangkat cerdas, setiap entitas yang mampu menguasai teknologi ini disitulah keunggulan kompetitifnya (competitive advantage). Namun ditengah perkembangan industri 4.0 yang cukup masif pemerintah harus bergerak cepat dalam mengadopsi platform ini, jika tidak, mereka akan menurunkan efisiensi proses bisnis untuk menjaga stabilitas layanan publik. Oleh sebab itu diperlukan keilmuan dan pemahaman yang benar bagi pemerintah dalam menghadapai era Industri 4.0, dimana Chief Information Officer (CIO) dapat mengambil peranan penting dalam memberikan dukungan yang didasari atas keilmuan mereka terkait tren teknologi industri 4.0, khususnya AI yang telah banyak diadopsi di berbagai sektor.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

Survelliance of Type I and II Diabetic Subjects on Physical Characteristics

2021 ◽  
pp. 308-344
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Parul Singhal
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Electronic Health Records ◽  
Analysis Data ◽  
Healthcare Systems ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Diagnostic Information ◽  
Unstructured Data ◽  
Type I

The Delhi and NCR healthcare systems are rapidly registering electronic health records and diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing, and large quantities of information are examined and new insights are part of the analysis of this technology experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. The authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increasing with age in case of males and not following the pattern of diabetes variation with age, while in the case of females, TTH pattern variation is the same as diabetes (i.e., increasing trend up to age of 60 then decreasing).

scholarly journals Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Mei Yang ◽  
Shah Nazir ◽  
Qingshan Xu ◽  
Shaukat Ali
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Multicriteria Decision Making ◽  
Literature Study ◽  
Multicriteria Decision ◽  
Challenging Tasks ◽  
Critical Issues ◽  
Different Shapes

The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based decision-making have effective applications in big data. Derivations are made based on the use of deep algorithms and multicriteria. Due to its effectiveness and potentiality, it is exploited in several domains such as computer science and information technology, agriculture, and business sector. The aim of the proposed study is to present a systematic literature study in order to show the applications of deep learning algorithms and multicriteria decision approaches for the problems of big data. The research finds novel means to make the decision support system for the problems of big data using multiple criteria in integration with machine learning and artificial intelligence approaches.

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