Applications of Artificial Intelligence in e-Healthcare Systems

Applications of Artificial Intelligence in e-Healthcare Systems /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Sabharwal, Munish (Editor), Baluswamy, B. Balamurugan (Editor), Kumar, S. Rakesh (Editor), Gayathri, N. (Editor), Suvanov, Shakhzod (Editor)
Format: Electronic eBook
Language:English
Published: Stevenage : Institution of Engineering & Technology, 2022.
Series:Healthcare technologies series ; 40.
Subjects:
Artificial intelligence > Medical applications.
Medical informatics.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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245 0 0 |a Applications of Artificial Intelligence in e-Healthcare Systems /  |c edited by, Munish Sabharwal, B. Balamurugan Baluswamy, S. Rakesh Kumar, N. Gayathri and Shakhzod Suvanov 
264 1 |a Stevenage :  |b Institution of Engineering & Technology,  |c 2022. 
264 4 |c ©2022 
300 |a 1 online resource ( xii, 290 pages) :  |b illustrations. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Healthcare technologies series ;  |v 40 
504 |a Includes bibliographical references and an index. 
505 0 0 |a Machine generated contents note:   |g 1.  |t Introduction to AI in E-healthcare /  |r S. Jerald Nirmal Kumar --   |g 1.1.  |t Introduction to artificial intelligence --   |g 1.2.  |t Machine learning --   |g 1.3.  |t Deep learning --   |g 1.4.  |t AI timeline in the healthcare sector --   |g 1.4.1.  |t AI discovery and the development of drugs --   |g 1.4.2.  |t AI personalized healthcare --   |g 1.5.  |t AI devices in healthcare --   |g 1.5.1.  |t Role of AI in healthcare --   |g 1.6.  |t Framework for AI in healthcare using AI devices --   |g 1.6.1.  |t Step 1: Analytic integration --   |g 1.6.2.  |t Step 2: Choose/build predictive models --   |g 1.6.3.  |t Step 3: Optimizing predictive models --   |g 1.6.4.  |t Step 4: Retrospective comparison --   |g 1.6.5.  |t Step 5: Prescriptive optimization --   |g 1.7.  |t Support smart devices and applications --   |g 1.8.  |t IoT devices --   |g 1.9.  |t Backend facilitator --   |g 1.10.  |t Architecture design for an e-healthcare system --   |g 1.10.1.  |t UI layers --   |g 1.10.2.  |t Information-handling layer --   |g 1.10.3.  |t Remote diagnoses and guidelines --   |g 1.10.4.  |t Data exchange machine --   |g 1.11.  |t Bigdata devices (storage) --   |g 1.12.  |t Data mining --   |g 1.13.  |t AI assistance for surgical robotics --   |g 1.14.  |t AI e-healthcare risk factors m --   |g 1.14.1.  |t Risk factors in supporting AI e-healthcare --   |g 1.14.2.  |t Patient e-record security applications --   |g 1.15.  |t Challenges to the use of AI devices in healthcare --   |g 1.16.  |t AI devices and managing healthcare data --   |g 1.17.  |t AI in e-healthcare applications --   |g 1.17.1.  |t Overview of AI applications in e-healthcare --   |t References --   |g 2.  |t scope and future outlook of artificial intelligence in healthcare systems /  |r Kalaiarasi Sonai Muthu Anbananthen --   |g 2.1.  |t Introduction --   |g 2.1.1.  |t Importance of AI in healthcare --   |g 2.1.2.  |t Life cycle approach to AI --   |g 2.2.  |t Leadership and oversight --   |g 2.2.1.  |t Standards and regulation --   |g 2.3.  |t AI and machine learning are entering a new era --   |g 2.4.  |t Exploring the clinical value of AI --   |g 2.4.1.  |t Ecosystem --   |g 2.5.  |t How is AI transforming the healthcare industry? --   |g 2.5.1.  |t Digital consultation --   |g 2.5.2.  |t Smart diagnosis --   |g 2.5.3.  |t Drug discovery --   |g 2.5.4.  |t Robotic assistance --   |g 2.5.5.  |t Virtual follow-up system --   |g 2.6.  |t Potential of AI in various fields of healthcare systems --   |g 2.6.1.  |t Comparison of various fields of healthcare using AI technology --   |g 2.6.2.  |t Good at-risk phase --   |g 2.6.3.  |t Acute care phase --   |g 2.6.4.  |t Chronic care process --   |g 2.7.  |t Analyzing the priority areas in healthcare systems --   |g 2.8.  |t Challenges associated with the implementation of an Al-driven healthcare system --   |g 2.8.1.  |t Regulatory challenges --   |g 2.8.2.  |t Standardization challenges --   |g 2.8.3.  |t Ethical and social challenges --   |g 2.8.4.  |t Challenges for a transforming discipline --   |g 2.9.  |t Vision and future potential of AI in healthcare --   |g 2.10.  |t Conclusion --   |t References --   |g 3.  |t Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays /  |r K. Karunamurthy --   |g 3.1.  |t Introduction --   |g 5.1.  |t Keiatea woncs --   |g 3.3.  |t Methods --   |g 3.3.1.  |t Data preprocessing --   |g 3.3.2.  |t Proposed methodology --   |g 3.4.  |t Results and discussion --   |g 3.4.1.  |t Databases --   |g 3.4.2.  |t Performance metrics --   |g 3.4.3.  |t Results reported --   |g 3.4.4.  |t Comparison --   |g 3.5.  |t Conclusion --   |t References --   |g 4.  |t Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity /  |r N. Gayathri --   |g 4.1.  |t Introduction --   |g 4.2.  |t Related works --   |g 4.3.  |t Neural network --   |g 4.3.1.  |t Artificial neuron --   |g 4.3.2.  |t Spiral basis function neural network --   |g 4.3.3.  |t Multilayer perceptron --   |g 4.3.4.  |t Long- and short-term memory --   |g 4.3.5.  |t Modular neural network --   |g 4.3.6.  |t Sequence-to-sequence models --   |g 4.4.  |t Convolutional neural network --   |g 4.4.1.  |t Deep convolutional neural network --   |g 4.4.2.  |t Convolutional layer --   |g 4.5.  |t Simulation and analysis --   |g 4.6.  |t Conclusion --   |t References --   |g 5.  |t automated NLP methodology to predict ICU mortality CLINICAL dataset using mulriclass grouping with LSTM RNN approach /  |r S. Rakesh Kumar --   |g 5.1.  |t Introduction to natural language processing --   |g 5.1.1.  |t Text representation --   |g 5.1.2.  |t Medical field impacted using NLP --   |g 5.1.3.  |t NLP - a driven resource for healthcare to improve outcomes --   |g 5.1.4.  |t LSTM-RNN a novel approach in prediction --   |g 5.1.6.  |t RNN --   |g 5.2.  |t Data collection --   |g 5.2.1.  |t Multiclass feature analysis --   |g 5.3.  |t Proposed system --   |g 5.4.  |t Results and discussion --   |g 5.5.  |t Conclusion --   |t References --   |g 6.  |t Applying machine learning techniques to build a hybrid machine learning model for cancer prediction /  |r S. Rakesh Kumar --   |g 6.1.  |t Introduction --   |g 6.2.  |t Literature review --   |g 6.3.  |t Dataset description --   |g 6.4.  |t System methodology --   |g 6.4.1.  |t Dataset analysis --   |g 6.4.2.  |t Splitting of the dataset and preprocessing --   |g 6.4.3.  |t Training and testing the dataset using HMLM --   |g 6.4.4.  |t Evaluation metrics --   |g 6.4.5.  |t Output interpretation --   |g 6.4.6.  |t Result analysis --   |g 6.5.  |t Conclusion --   |g 6.6.  |t Future work --   |t References --   |g 7.  |t AI in healthcare: challenges and opportunities /  |r Mithileysh Sathiyanarayanan --   |g 7.1.  |t Introduction --   |g 7.1.1.  |t Progressive way of life: Healthcare 4.0 --   |g 7.2.  |t Development of Healthcare 4.0 --   |g 7.2.1.  |t Evolution of Industry 4.0 --   |g 7.2.2.  |t Evolution of Healthcare 4.0 --   |g 7.3.  |t Development of AI in the healthcare sector --   |g 7.3.1.  |t Areas in which AI is used across healthcare --   |g 7.4.  |t AI challenges in healthcare --   |g 7.5.  |t AI developments in healthcare --   |g 7.6.  |t AI opportunities in healthcare --   |g 7.7.  |t Discussion and conclusion --   |t References --   |g 8.  |t Impression of artificial intelligence in e-healthcare medical applications /  |r P. Bhuvanashree --   |g 8.1.  |t Introduction --   |g 8.2.  |t E-Healthcare --   |g 8.3.  |t Application of e-Healthcare --   |g 8.3.1.  |t Application of telemedicine --   |g 8.3.2.  |t Telehealth (upcoming years) --   |g 8.4.  |t Artificial intelligence --   |g 8.5.  |t Significant advancements of technology --   |g 8.6.  |t Artificial intelligence in medical sector --   |g 8.6.1.  |t Artificial intelligence and robotics are transforming healthcare --   |g 8.7.  |t Pros of artificial intelligence in healthcare --   |g 8.8.  |t Cons of artificial intelligence in healthcare --   |g 8.9.  |t Discussion and conclusion --   |t References --   |g 9.  |t Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset /  |r R. Suchithra --   |g 9.1.  |t Introduction --   |g 9.2.  |t Related work --   |g 9.3.  |t Methodology --   |g 9.3.1.  |t Gathering of the dataset --   |g 9.3.2.  |t Data cleaning --   |g 9.3.3.  |t Attribute/feature selection through MLSCO --   |g 9.3.4.  |t Prediction of risk using HRCC --   |g 9.4.  |t Result and examination --   |g 9.4.1.  |t Examination parameters --   |g 9.4.2.  |t Experimental examination --   |g 9.5.  |t Conclusion --   |t References --   |g 10.  |t narrative review and impacts on trust for data in the healthcare industry using artificial intelligence /  |r M. Sivakumar --   |g 10.1.  |t Introduction --   |g 10.2.  |t Hypotheses development --   |g 10.2.1.  |t Roles for healthcare artificial intelligence --   |g 10.3.  |t inconvenient truth about AI in healthcare --   |g 10.4.  |t Role of cloud storage with AI in healthcare --   |g 10.5.  |t Finally grasping the enormous potential of AI in healthcare --   |g 10.5.1.  |t Prediction --   |g 10.5.2.  |t Diagnosis --   |g 10.5.3.  |t Personalized treatment options and behavioral interventions --   |g 10.5.4.  |t Drug discovery --   |g 10.6.  |t Several key challenges to the integration of healthcare and AI --   |g 10.6.1.  |t Understanding the gap --   |g 10.6.2.  |t Fragmented data --   |g 10.6.3.  |t Appropriate security --   |g 10.6.4.  |t Data governance --   |g 10.6.5.  |t Software --   |g 10.7.  |t Data exploration in healthcare for AI --   |g 10.7.1.  |t Data cleansing --   |g 10.7.2.  |t Data that are inconsistent or duplicate --   |g 10.7.3.  |t Exploring anomalies in the data --   |g 10.8.  |t Starting the cleaning up of typographical errors, clearing the values, and perfecting the formatting --   |g 10.8.1.  |t Aggregation --   |g 10.8.2.  |t Decomposition --   |g 10.8.3.  |t Encoding --   |g 10.9.  |t Artificial intelligence (AI) in healthcare using an open science approach --   |g 10.9.1.  |t What is the difference between open data and open research? --   |g 10.10.  |t Conclusion --   |t References --   |g 11.  |t Analysis of COVID-19 outbreak using data visualization techniques: a review /  |r Shalini --   |g 11.1.  |t Introduction --   |g 11.2.  |t Methodology --   |g 11.2.1.  |t Objective --   |g 11.2.2.  |t Method --   |g 11.2.3.  |t About dataset --   |g 11.3.  |t Datewise analysis --   |g 11.3.1.  |t Analysis of recovery rate (RR) and mortality rate (MR) throughout the world --   |g 11.4.  |t Growth factor --   |g 11.5.  |t Countrywise analysis --   |g 11.5.1.  |t Journey of different countries in COVID-19 --   |g 11.5.2.  |t Proportion of each nation in CC, RC, and DC --   |g 11.6.  |t Clustering of countries --   |g 11.6.1.  |t Weekly data analysis for India --   |g 11.6.2.  |t Datewise/daily data analysis for India with comparison --   |g 11.7.  |t Machine learning models for prediction --   |g 11.7.1.  |t Linear regression model prediction for confirmed cases --   |g 11.7.2.  |t Polynomial regression for prediction of CC --   |g 11.7.3.  |t SVM model regression for prediction of CC --   |g 11.7.4.  |t Holt's linear model --   |g 11.7.5.  |t Holt's winter model for everyday time series --   |g 11.7.6.  |t AR model (using AUTO ARIMA) --   |g 11.7.7.  |t MA model (using AUTO ARIMA) --   |g 11.7.8.  |t ARIMA model (using AUTO ARIMA) --   |g 11.7.9.  |t SARIMA model (using AUTO ARIMA) --   |g 11.7.10.  |t Facebook's Prophet model for forecasting --   |g 11.8.  |t Forecasting results and summarizations using various models --   |g 11.8.1.  |t Time-Series forecasting for DC --   |g 11.9.  |t Conclusion --   |t References --   |g 12.  |t Artificial intelligence-based electronic health records for healthcare /  |r Gaurav Dhuriya --   |g 12.1.  |t Introduction --   |g 12.1.1.  |t Overview of artificial intelligence --   |g 12.1.2.  |t E-healthcare and electronic health records --   |g 12.2.  |t AI in E-healthcare structure --   |g 12.2.1.  |t Use of AI in E-healthcare structure -- 
505 0 0 |a Contents note continued:   |g 12.2.2.  |t Architecture of data retrieval and data processing in electronic health records (Figure 12.3) --   |g 12.3.  |t Smart devices pre-owned in electronic health documentations --   |g 12.3.1.  |t Health documentations construct use of wearable devices --   |g 12.3.2.  |t Monitoring forbearing's construct use of smart contract --   |g 12.4.  |t Care and privacy of healthcare data --   |g 12.4.1.  |t Care challenges --   |g 12.4.2.  |t Care and protection highlights of current EHR frameworks --   |g 12.4.3.  |t Data innovation care episodes in medical care position --   |g 12.5.  |t Conclusion --   |t References --   |g 13.  |t Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing /  |r S. Rakesh Kumar --   |g 13.1.  |t Introduction --   |g 13.2.  |t Natural language processing --   |g 13.2.1.  |t NLP techniques --   |g 13.2.2.  |t Sentiment analysis --   |g 13.2.3.  |t Language translation --   |g 13.2.4.  |t Text extraction --   |g 13.2.5.  |t Chatbox --   |g 13.3.  |t Machine learning for NLP --   |g 13.3.1.  |t Unsupervised machine learning --   |g 13.3.2.  |t Concept Matrix --   |g 13.3.3.  |t Syntax Matrix --   |g 13.3.4.  |t Syntax information --   |g 13.3.5.  |t Hybrid Machine Learning Systems for NLP --   |g 13.4.  |t Ultrasound devices --   |g 13.5.  |t Results and analysis --   |g 13.6.  |t Conclusion --   |t References. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 0 |a Print record version. 
650 0 |a Artificial intelligence  |x Medical applications. 
650 0 |a Medical informatics. 
700 1 |a Sabharwal, Munish,  |e editor. 
700 1 |a Baluswamy, B. Balamurugan,  |e editor. 
700 1 |a Kumar, S. Rakesh,  |e editor. 
700 1 |a Gayathri, N.,  |e editor. 
700 1 |a Suvanov, Shakhzod,  |e editor. 
710 2 |a ProQuest (Firm) 
776 0 8 |i Print version:  |a Sabharwal, Munish  |t Applications of Artificial Intelligence in e-Healthcare Systems  |d Stevenage : Institution of Engineering & Technology,c2022  |z 9781839534492 
830 0 |a Healthcare technologies series ;  |v 40. 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=7015272  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 0 
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919 |a .ulebk  |b 2022-07-07 
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