Computational Intelligence and Its Applications in Healthcare

By Sudip Paul

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.

• Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications
• Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms
• Presents the latest developments in computational methods in healthcare
• Bridges the gap between obsolete literature and current literature

• Language: English
• Publisher: Academic Press
• Release date: Aug 1, 2020
• ISBN: 9780128206195

Book preview

9780128206195_FC

Computational Intelligence and Its Applications in Healthcare

First Edition

Jitendra Kumar Verma

Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gurugram (Manesar), India

Sudip Paul

Department of Biomedical Engineering, School of Technology, North-Eastern Hill University (NEHU), Shillong, India

Prashant Johri

Professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India

Table of Contents

Cover image

Title page

Copyright

Contributors

1: The impact of Internet of Things and data semantics on decision making for outpatient monitoring

Abstract

Acknowledgments

1: Introduction

2: Related works

3: Scenarios and states in the measurement process

4: Describing the measurement and its underlying semantics

5: Perspectives on IoT devices in data-stream processing

6: Monitoring outdoor activities of a patient: Application case

7: Conclusions

2: Deep-learning approaches for health care: Patients in intensive care

Abstract

1: Introduction

2: Literature review

3: Material and methods

4: Implementation and results

5: Discussion and conclusion

3: Brain MRI image segmentation using nature-inspired Black Hole metaheuristic clustering approach

Abstract

1: Introduction

2: Related work

3: Proposed framework

4: Experimental results and discussion

5: Conclusion

4: Blockchain for public health: Technology, applications, and a case study

Abstract

1: Introduction

2: What is blockchain?

3: Benefits of blockchain application in public health

4: A use case from Estonia

5: Conclusion and challenges

5: Compression and multiplexing of medical images using optical image processing

Abstract

1: Introduction

2: Theory

3: Methodology

4: Result

5: Conclusion

6: Analysis of skin lesions using machine learning techniques

Abstract

1: Introduction

2: Related works

3: Materials and methods

4: Results and discussion

5: Conclusion

7: Computational intelligence using ontology—A case study on the knowledge representation in a clinical decision support system

Abstract

1: Introduction

2: Clinical decision support systems

3: Computational semantics

4: Discussion and conclusion

8: Neural network-based abnormality detection for electrocardiogram time signals

Abstract

1: Introduction

2: Electrocardiogram signal analysis

3: Deep recurrent neural network model

4: Network architecture of long short-term neural network

5: Training of data

6: Result analysis

7: Conclusion

9: Machine learning approaches for acetic acid test based uterine cervix image analysis

Abstract

1: Introduction

2: Related work

3: Methodology

4: Results and discussions

5: Conclusion

10: Convolutional neural network for biomedical applications

Abstract

1: Introduction

2: Introduction to ML techniques

3: Why DL algorithm?

4: Medical images and neural networks

5: Types of neural networks

6: Deep learning approach in medical area

7: Building blocks of neural network

8: Deep learning and medical imaging

9: Conclusion

11: Alzheimer’s disease classification using deep learning

Abstract

1: Computational intelligence

2: Artificial intelligence vs computational intelligence

3: Artificial intelligence and the evolution toward deep learning

4: Alzheimer’s disease

5: Technical limitations and scope of Alzheimer’s disease diagnosis

6: Relevance of deep learning in Alzheimer’s disease diagnosis

7: Deep learning

8: Convolutional neural network

9: Applications of deep learning

10: A review of Alzheimer’s disease classification using deep learning

11: Supporting software

12: Conclusions

12: Diabetic retinopathy identification using autoML

Abstract

1: Introduction

2: Related work

3: Materials and methods

4: Results and discussion

5: Conclusion

13: Knowledge-based systems in medical applications

Abstract

1: Introduction

2: Data in health care

3: Factors influencing medical decisions

4: Structure of medical decisions

5: Knowledge-based systems in medicine: Architecture and working

6: Case studies of medical knowledge-based systems

7: Examples of renowned medical knowledge-based systems

8: Knowledge-based medical systems—Pros and cons

9: Conclusion

14: REMOVED: Convolution neural network-based feature learning model for EEG-based driver alert/drowsy state detection

Abstract

15: Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification

Abstract

1: Introduction

2: Related works

3: Proposed method

4: Results

5: Conclusions and future work

Index

Copyright

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Contributors

Gilu K. Abraham     Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Kochi, Kerala, India

Jyoti S. Bali     KLE Technological University, Hubballi, Karnataka, India

J. Bethanney Janney     Department of Biomedical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

Preethi Bhaskaran     Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Kochi, Kerala, India

Kallol Bhattacharya     Department of Applied Optics and Photonics, Kolkata, West Bengal, India

D. Baskar     Department of Electronics and Communication, Hindustan College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Mario José Diván     Data Science Research Group, Economy School, National University of La Pampa, Santa Rosa, La Pampa, Argentina

Sachin Dubey     Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

Ashima Gambhir     Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Haryana, Gurugram, India

H.M. Gireesha     KLE Technological University, Hubballi, Karnataka, India

Prince Gupta     Netaji Subhash University of Technology, Delhi, India

Raj Kuwar Gupta     Soft Computing and Expert System Laboratory, Atal Bihari Vajpayee - Indian Institute of Information Technology and Management, Gwalior, MP, India

Shyamala Guruvare     Department of Obstetrics and Gynecology, Kasturba Medical College, Manipal, India

V.K. Harikrishnan     Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Haryana, Gurugram, India

V.S. Jayanthi     Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Kochi, Kerala, India

D. Jeyabharathi     Department of Information Technology, Sri Krishna College of Technology, Coimbatore, India

Prashant Johri     School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

N.V. Kousik     School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

Vidya Kudva

Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal

NMAM Institute of Technology (An Autonomous Institution Affiliated to VTU, Belagavi), Nitte, India

S. Krishna Kumar     Department of Biomedical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

K.S. Lavanya     Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, India

Ravi Lourdusamy     Sacred Heart College (Autonomous), Tirupattur, Tamil Nadu, India

Saumil Maheshwari     Soft Computing and Expert System Laboratory, Atal Bihari Vajpayee - Indian Institute of Information Technology and Management, Gwalior, MP, India

Xavierlal J. Mattam     Sacred Heart College (Autonomous), Tirupattur, Tamil Nadu, India

A.V. Nandi     KLE Technological University, Hubballi, Karnataka, India

P.C. Nissimagoudar     KLE Technological University, Hubballi, Karnataka, India

Anirban Patra     Department of ECE, JIS College of Engineering, Kalyani, West Bengal, India

Keerthana Prasad     Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India

E.L. Dhivya Priya     Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, India

R. Arshath Raja     Research and Development, ICT Academy, Chennai, India

S. Emalda Roslin     Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

Arijit Saha     Department of ECE, B. P. Poddar IMT, Kolkata, West Bengal, India

María Laura Sánchez-Reynoso     Data Science Research Group, Economy School, National University of La Pampa, Santa Rosa, La Pampa, Argentina

Deepak Saxena     Trinity Centre for Digital Business, Trinity College Dublin, Dublin, Ireland

Anupam Shukla     Indian Institute of Information Technology, Pune, Maharashtra, India

Blessy C. Simon     Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Kochi, Kerala, India

Saurabh Ranjan Srivastava     Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

Pankaj Upadhyay     Computer Engineering Department, NIT Kurukshetra, Kurukshetra, Haryana, India

Jitendra Kumar Verma     Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University, Gurugram (Manesar), India

Meenu Vijarania     Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Haryana, Gurugram, India

N. Yuvaraj     Research and Development, ICT Academy, Chennai, India

1: The impact of Internet of Things and data semantics on decision making for outpatient monitoring

Mario José Diván; María Laura Sánchez-Reynoso    Data Science Research Group, Economy School, National University of La Pampa, Santa Rosa, La Pampa, Argentina

Abstract

The Internet of Things (IoT) is useful for data collection due to its portability, low cost, and the wide range of heterogeneous devices available for field deployment. Outpatients are a natural group to be monitored using IoT devices. Data semantics is a key topic in this regard, related mainly to risk prevention and support for data-driven decision making. In this chapter, an application of a measurement framework with the support of states and scenarios is introduced as a mean of homogenizing data semantics independently of the collector device. The application is focused on monitoring of outpatients engaged in physical activities outdoor. The project definition is detailed, along with the devices used in the implementation. The main contribution of this work is the integration of heterogeneous IoT devices through a measurement framework with the support of multiple scenarios and states, with data semantics used to guide the real-time data processing.

Keywords

Internet of Things; Data; Decision making; Real-time; Monitoring

Acknowledgments

This research is partially supported by the projects Res.CD 278/2016 and 312/18 of the Economics and Law School of the National University of La Pampa.

1: Introduction

The Internet of Things (IoT) has allowed the implementation of different strategies for real-time monitoring that strengthen data-driven decision making and the use of recommender systems [1]. One of the most important strengths of the IoT is the ability to integrate different hardware in a coordinated way in order to implement various data- collecting strategies. However, this strength implies heterogeneity, and data interoperability (from syntactic and semantic points of view) constitutes a current challenge in this field [2].

Health care is an ideal field for applying the IoT for patient monitoring, due to a plethora of available, cheap, usable, and wearable sensors. It is possible to coordinate all of these sensor devices under a processing strategy for orchestrating data collection, in order to prevent and avoid different risk situations [3]. Furthermore, this collecting strategy can be articulated with a recommender system, with the aim of providing suggestions to be implemented when a given situation (e.g., hypertension) is detected [4]. In this context, the recommendations imply a way to incorporate previous experience and knowledge to support decisions.

This chapter describes an integral perspective on IoT devices as a collection method in outpatient monitoring, supported by a measurement framework able to model states and scenarios, using a data- stream processing architecture for supporting data-driven decision making, along with an alternative for implementing recommendations based on the knowledge gained. Thus an application of a measurement framework with the support of states and scenarios is introduced as a mean of homogenizing the data semantics independently of the collection devices. The application focuses on outpatient monitoring of outdoor physical activities. The project definition is detailed herein, along with the devices used.

The work is organized into seven sections. Section 2 introduces some related works. Section 3 provides a synthesis of the underlying ideas associated with the measurement and evaluation (M&E) framework, along with the state and scenario modeling. Section 4 defines the M&E project, specifying the entity being monitored, the characteristics to be monitored, the associated methods, and the scenarios and states to be used in the application case. Section 5 introduces the method by which the data processing is data guided, discriminating the data meaning through tags. Section 6 discusses the application case of outpatient monitoring of outdoor physical activities. Finally, some conclusions and future work suggestions are outlined.

2: Related works

In [5] a synthesis is given of the IoT, its use in health care, and the use of machine learning for integrating data coming from a variety of sensors. The data semantics was brought onboard through a data-aware annotation strategy that allowed guiding the data meaning in relation to its processing and the application of machine-learning algorithms. Our proposal models the data differently, as a part of heterogeneous data streams flowing continuously, which are processed, analyzed, and discarded on-the-fly, with appropriate recommendations being provided.

Kaur et al. [6] proposed to use historical data jointly with data coming from IoT devices in health care in order to provide context-aware recommendations developing an active behavior. The integration proposed by the authors is interesting because it simultaneously considers IoT, historical data, the decision-making process, and the recommendation strategy. On the other hand, in our proposal, the measures coming from heterogeneous devices are completely based on a measurement and evaluation framework.

In [7] missing values and their effects on the decision-making process are addressed. The context is pregnancy monitoring, in which the vital signs need to be analyzed over 24 h every day of the week. In this environment, the authors propose a very interesting approach oriented to real-time data monitoring based on IoT devices. Our data-collecting strategy is different, as it is based on semantic annotations for identifying data meaning coming from each sensor.

In [8] an architecture is described for solving different situations related to telemedicine using IoT devices in the patient layer. The architecture contemplates the patient’s sensors as an initial approach to their current situation, while the same information is used by an algorithm for detecting the associated level of risk. Once the level of risk has been determined, the course of action and recommendations are provided immediately, avoiding the risks. Our proposal introduces metadata related to project definition for informing the data meaning related to each measure coming from a sensor. In addition, previous experience and knowledge are kept in an organizational memory organized by cases.

In [9] an integrated proposal for monitoring patients suffering obesity is introduced. A device based on the Arduino platform is used to collect data on body temperature, heart rate, blood pressure, and level of oxygen in the blood, with data jointly stored in the Arduino and on the Cloud, to provide monitoring services to the medical staff. Patients are monitored throughout the day during each activity engaged in, being under continuous monitoring. As a different approach, our proposal uses a measurement framework based on an ontology to support data meaning, jointly with its processing, storage, and use.

In [10] the possibility of image collecting is proposed, using sensor edge computing to provide a certain level of security in outpatient monitoring. In this way, outpatients contain their data as close as possible to themselves, providing a decrease in the levels of risk related to privacy and the data being viewed by others. Similarly, our proposal contemplates the use of complementary data to measurements in different formats (e.g., images, video, audio, etc.). A particular hazard of data collected in medical continuous monitoring systems is that of privacy, which continues to be a challenge to solve. Our proposal is different in that it uses various tags based on an ontology for properly discriminating each piece of data used and transmitted.

In [11] an architecture for supporting self-rehabilitation in elderly patients based on data-stream applications was introduced. The proposal discusses the monitoring of each elderly patient during rehabilitation exercises at home, while the application collects data and informs them in real time. In this context, the type of exercise to be monitored depends on the rehabilitation strategy of each outpatient, as the activity patterns of all the outpatients being monitored are not identical. Our proposal, on the other hand, specializes in measurement and evaluation projects incorporating decision criteria based on scenarios and entity states, for self-interpreting the received measurements and providing recommendations immediately when an atypical situation is detected.

3: Scenarios and states in the measurement process

The reliability of a measurement process depends on its ability to be transparently and homogeneously reproduced, with the results being comparable. For that reason, the measurement frameworks are important in defining and describing both the aim and the context of the measurement itself [12]. Our data-processing strategy specializes in measurement and evaluation projects and is based on a measurement framework [13, 14] in which each concept to be monitored is defined as an entity (e.g., an athlete) that belongs to an entity category (e.g., athletes). Each entity is contained in an environment (i.e., the context). Entities being monitored, along with their contexts, are discretely characterized through attributes and context properties, respectively. For example, the study of the athlete's activities could be addressed through the following attributes heart rate, systolic pressure, diastolic pressure, and respiration rate. The environment surrounding they could be described using the environmental temperature and humidity as context properties.

The number of attributes defines the dimensionality of the entity, while the number of context properties does the same for the environment. Thus, entity and context are described through the measurements related only to the indicated attributes and not others.

Each attribute and context property has its own behavior characterized by means of the data series, jointly varying throughout the measurement process. Because the variation range of attributes and context properties can be known (e.g., heart rate in an athlete depending on the age) or eventually estimated, different variation intervals can be established for them. Of course, each interval definition (be it for attributes or context properties) implies the assistance of an expert in the specific measurement domain (e.g., a doctor for the athlete’s monitoring).

Thus let A be the set of selected attributes (i.e., a1, a2, …, an) for describing the entity e and CP the subset of chosen context properties (i.e., p1, p2, …, pm) for characterizing its environment; it is then possible to define for each one a metric that quantifies them. Metrics for the attributes ai can be expressed, such as Mai, and each one defines the measurement method, device, unit, and scale. Consequently, metrics for context properties pi can be shown, such as Mpi. In this way, a measurement related to an entity being monitored in a given time will be a vector with its dimension being the sum of all attributes and context properties (i.e., n + m). Since continuous variables can be discretized through intervals, each metric (be it related to an attribute or context property) can be analyzed through mutually exclusive intervals defined by experts based on the analysis interest. Thus a state can be defined as the quantitative view expressed by an entity in terms of all its attributes in a given time (i.e., it is a composition of defined intervals for each attribute). By analogy, the scenario is a quantitative perspective for the entity’s environment represented as a composition of defined intervals for each context property.

A state machine can be defined for the entity’s states with the aim of analyzing the effect of the state’s transition in the entity. Each transition can have a weighting that eventually describes the level of importance associated with it. With the same thinking, another state machine can analyze the scenarios and the effect of each associated transition. Both state machines allow focusing on the effect of the entity’s states and scenarios in the interpretation of each measure. That is to say, while the entity state machine describes the potential states that each entity could eventually reach, the scenario state machine schematizes the different contextual alternatives in which the entity could be immersed. Thus the decision criteria used for interpreting each measure are context-aware and state-aware.

The interpretation of measures using decision criteria is known as an indicator. The indicator allows interpretation of the measure based on its current scenario and state, giving a contextualization to each value and providing a conclusion based on expert knowledge. Thus, the measurement process considers characteristics related to the entity, its context, the entity’s states, the scenario’s states, and the expert’s knowledge documented by the decision criteria of each associated indicator.

4: Describing the measurement and its underlying semantics

The goal of this section is to outline a monitoring project integrating IoT devices worn or placed on patients engaged in outdoor activities. The monitoring project is defined based on the measurement framework, which integrates IoT devices with data semantics, helping to guide the data processing depending on the entity under supervision. The project’s aim is to monitor the vital signs of patients engaged in outdoor activities. The entity category is bound to patients who need to undergo gradual rehabilitation through walking. The entity to be monitored is defined as a patient engaged in outdoor activities. For characterizing the patient’s response to the outdoor activities, experts have defined as critical the monitoring of heart rate jointly with the number of steps taken by a patient, in order to detect sedentarism. In terms of the context, experts consider critical the monitoring of temperature and humidity, mainly thinking of their potential effects on the patient engaged in physical outdoor activities. Thus on one side, the entity’s attributes are defined as heart rate and walking rhythm, and on the other side, the context properties are associated with the environmental temperature and humidity.

The next step is to define the way in which each attribute or context property is to be quantified. Two direct metrics are defined for attributes: (i) Value of the heart rate: This value is obtained using an optical method at a close distance on