Applications of Artificial Intelligence in E-Healthcare Systems

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Contents
About the editors
1 Introduction to AI in E-healthcare
Abstract
1.1 Introduction to artificial intelligence
1.2 Machine Learning
1.3 Deep Learning
1.4 AI timeline in the healthcare sector
1.4.1 AI discovery and the development of drugs
1.4.2 AI personalized healthcare
1.5 AI devices in healthcare
1.5.1 Role of AI in healthcare
1.6 Framework for AI in healthcare using AI devices
1.6.1 Step 1: Analytic integration
1.6.2 Step 2: Choose/build predictive models
1.6.3 Step 3: Optimizing predictive models
1.6.4 Step 4: Retrospective comparison
1.6.5 Step 5: Prescriptive optimization
1.7 Support smart devices and applications
1.8 IoT devices
1.9 Backend facilitator
1.10 Architecture design for an e-healthcare system
1.10.1 UI layers
1.10.2 Information-handling layer
1.10.3 Remote diagnoses and guidelines
1.10.4 Data exchange machine
1.11 Bigdata devices (storage)
1.12 Data mining
1.13 AI assistance for surgical robotics
1.14 AI e-healthcare risk factors
1.14.1 Risk factors in supporting AI e-healthcare
1.14.2 Patient e-record security applications
1.15 Challenges to the use of AI devices in healthcare
1.16 AI devices and managing healthcare data
1.17 AI in e-healthcare applications
1.17.1 Overview of AI applications in e-healthcare
References
2 The scope and future outlook of artificial intelligence in healthcare systems
Abstract
2.1 Introduction
2.1.1 Importance of AI in healthcare
2.1.2 Life cycle approach to AI
2.2 Leadership and oversight
2.2.1 Standards and regulation
2.3 AI and machine learning are entering a new era
2.4 Exploring the clinical value of AI
2.4.1 Ecosystem
2.5 How is AI transforming the healthcare industry?
2.5.1 Digital consultation
2.5.2 Smart diagnosis
2.5.3 Drug discovery
2.5.4 Robotic assistance
2.5.5 Virtual follow-up system
2.6 Potential of AI in various fields of healthcare systems
2.6.1 Comparison of various fields of healthcare using AI technology
2.6.2 Good at-risk phase
2.6.3 Acute care phase
2.6.4 Chronic care process
2.7 Analyzing the priority areas in healthcare systems
2.8 Challenges associated with the implementation of an AI-driven healthcare system
2.8.1 Regulatory challenges
2.8.2 Standardization challenges
2.8.3 Ethical and social challenges
2.8.4 Challenges for a transforming discipline
2.9 Vision and future potential of AI in healthcare
2.10 Conclusion
References
3 Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays
Abstract
3.1 Introduction
3.2 Related works
3.3 Methods
3.3.1 Data preprocessing
3.3.2 Proposed methodology
3.4 Results and discussion
3.4.1 Databases
3.4.2 Performance metrics
3.4.3 Results reported
3.4.4 Comparison
3.5 Conclusion
References
4 Drug discovery clinical trial exploratory process and bioactivity analysis optimizer using deep convolutional neural network for E-prosperity
Abstract
4.1 Introduction
4.2 Related works
4.3 Neural network
4.3.1 Artificial neuron
4.3.2 Spiral basis function neural network
4.3.3 Multilayer perceptron
4.3.4 Longand short-term memory
4.3.5 Modular neural network
4.3.6 Sequence-to-sequence models
4.4 Convolutional neural network
4.4.1 Deep convolutional neural network
4.4.2 Convolutional layer
4.5 Simulation and analysis
4.6 Conclusion
References
5 An automated NLP methodology to predict ICU mortality CLINICAL dataset using multiclass grouping with LSTM RNN approach
Abstract
5.1 Introduction to natural language processing
5.1.1 Text representation
5.1.2 Medical field impacted using NLP
5.1.3 NLP – a driven resource for healthcare to improve outcomes
5.1.4 LSTM–RNN a novel approach in prediction
5.1.5 RNN
5.2 Data collection
5.2.1 Multiclass feature analysis
5.3 Proposed system
5.4 Results and discussion
5.5 Conclusion
References
6 Applying machine learning techniques to build a hybrid machine learning model for cancer prediction
Abstract
6.1 Introduction
6.2 Literature review
6.3 Dataset description
6.4 System methodology
6.4.1 Dataset analysis
6.4.2 Splitting of the dataset and preprocessing
6.4.3 Training and testing the dataset using HMLM
6.4.4 Evaluation metrics
6.4.5 Output interpretation
6.4.6 Result analysis
6.5 Conclusion
6.6 Future work
References
7 AI in healthcare: challenges and opportunities
Abstract
7.1 Introduction
7.1.1 Progressive way of life: Healthcare 4.0
7.2 Development of Healthcare 4.0
7.2.1 Evolution of Industry 4.0
7.2.2 Evolution of Healthcare 4.0
7.3 Development of AI in the healthcare sector
7.3.1 Areas in which AI is used across healthcare
7.4 AI challenges in healthcare
7.5 AI developments in healthcare
7.6 AI opportunities in healthcare
7.7 Discussion and conclusion
References
8 Impression of artificial intelligence in e-healthcare medical applications
Abstract
8.1 Introduction
8.2 e-Healthcare
8.3 Application of e-Healthcare
8.3.1 Application of telemedicine
8.3.2 Telehealth (upcoming years)
8.4 Artificial intelligence
8.5 Significant advancements of technology
8.6 Artificial intelligence in medical sector
8.6.1 Artificial intelligence and robotics are transforming healthcare
8.7 Pros of artificial intelligence in healthcare
8.8 Cons of artificial intelligence in healthcare
8.9 Discussion and conclusion
References
9 Heterogeneous recurrent convolution neural network for risk prediction in the EHR dataset
Abstract
9.1 Introduction
9.2 Related work
9.3 Methodology
9.3.1 Gathering of the dataset
9.3.2 Data cleaning
9.3.3 Attribute/feature selection through MLSCO
9.3.4 Prediction of risk using HRCC
9.4 Result and examination
9.4.1 Examination parameters
9.4.2 Experimental examination
9.5 Conclusion
References
10 A narrative review and impacts on trust for data in the healthcare industry using artificial intelligence
Abstract
10.1 Introduction
10.2 Hypotheses development
10.2.1 Roles for healthcare artificial intelligence
10.3 The inconvenient truth about AI in healthcare
10.4 Role of cloud storage with AI in healthcare
10.5 Finally grasping the enormous potential of AI in healthcare
10.5.1 Prediction
10.5.2 Diagnosis
10.5.3 Personalized treatment options and behavioral interventions
10.5.4 Drug discovery
10.6 Several key challenges to the integration of healthcare and AI
10.6.1 Understanding the gap
10.6.2 Fragmented data
10.6.3 Appropriate security
10.6.4 Data governance
10.6.5 Software
10.7 Data exploration in healthcare for AI
10.7.1 Data cleansing
10.7.2 Data that are inconsistent or duplicate
10.7.3 Exploring anomalies in the data
10.8 Starting the cleaning up of typographical errors, clearing the values, and perfecting the formatting
10.8.1 Aggregation
10.8.2 Decomposition
10.8.3 Encoding
10.9 Artificial intelligence (AI) in healthcare using an open science approach
10.9.1 What is the difference between open data and open research?
10.10 Conclusion
References
11 Analysis of COVID-19 outbreak using data visualization techniques: a review
Abstract
11.1 Introduction
11.2 Methodology
11.2.1 Objective
11.2.2 Method
11.2.3 About dataset
11.3 Datewise analysis
11.3.1 Analysis of recovery rate (RR) and mortality rate (MR) throughout the world
11.4 Growth factor
11.5 Countrywise analysis
11.5.1 Journey of different countries in COVID-19
11.5.2 Proportion of each nation in CC, RC, and DC
11.6 Clustering of countries
11.6.1 Weekly data analysis for India
11.6.2 Datewise/daily data analysis for India with comparison
11.7 Machine learning models for prediction
11.7.1 Linear regression model prediction for confirmed cases
11.7.2 Polynomial regression for prediction of CC
11.7.3 SVM model regression for prediction of CC
11.7.4 Holt’s linear model
11.7.5 Holt’s winter model for everyday time series
11.7.6 AR model (using AUTO ARIMA)
11.7.7 MA model (using AUTO ARIMA)
11.7.8 ARIMA model (using AUTO ARIMA)
11.7.9 SARIMA model (using AUTO ARIMA)
11.7.10 Facebook’s Prophet model for forecasting
11.8 Forecasting results and summarizations using various models
11.8.1 Time-Series forecasting for DC
11.9 Conclusion
References
12 Artificial intelligence-based electronic health records for healthcare
Abstract
12.1 Introduction
12.1.1 Overview of artificial intelligence
12.1.2 E-healthcare and electronic health records
12.2 AI in E-healthcare structure
12.2.1 Use of AI in E-healthcare structure
12.2.2 Architecture of data retrieval and data processing in electronic health records (Figure 12.3)
12.3 Smart devices pre-owned in electronic health documentations
12.3.1 Health documentations construct use of wearable devices
12.3.2 Monitoring forbearing’s construct use of smart contract
12.4 Care and privacy of healthcare data
12.4.1 Care challenges
12.4.2 Care and protection highlights of current EHR frameworks
12.4.3 Data innovation care episodes in medical care position
12.5 Conclusion
References
13 Automatic structuring on Chinese ultrasound report of Covid-19 diseases via natural language processing
Abstract
13.1 Introduction
13.2 Natural language processing
13.2.1 NLP techniques
13.2.2 Sentiment analysis
13.2.3 Language translation
13.2.4 Text extraction
13.2.5 Chatbox
13.3 Machine learning for NLP
13.3.1 Unsupervised machine learning
13.3.2 Concept Matrix
13.3.3 Syntax Matrix
13.3.4 Syntax information
13.3.5 Hybrid Machine Learning Systems for NLP
13.4 Ultrasound devices
13.5 Results and analysis
13.6 Conclusion
References
Index
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