Читать книгу Applied Smart Health Care Informatics - Группа авторов - Страница 11

Health care informatics, aka medical informatics, refers to the application of information engineering and management to the field of health care, which covers the management and use of patient health care information. By means of a multidisciplinary approach, it uses health information technology to improve health care by migrating to newer and higher quality opportunities. The United States National Library of Medicine (NLM) defines health informatics as “an interdisciplinary study of the design, development, adoption and application of IT‐based innovations in health care services delivery, management and planning.” Essentially, it affects the optimization of the acquisition, storage, retrieval, and use of information in health and bio‐medicine. Intelligent health care informatics augments the purview of existing health care amenities by adapting intelligent technologies to information engineering. Intelligent analysis of the information therein enhances the overall management as far as resource use is concerned.

With the advent of Big Data analysis, intelligent health care informatics has called for the efficient and effective use of healthcare data and the diagnosis thereof. During the next few years, there must be a sea change in the approaches to health care management. Smart pills may come to the foray as Bio‐MEMS drug delivery systems or intelligent drug delivery systems. Wearable medical devices could be attached to the patient's body to keep in touch with physicians for real time monitoring. Nano‐bots might be used to collect specimens or look for early signs of disease. Content management could also become more intelligent and intricate.

Patients with chronic disease live for decades through modern medication, surgery, close supervision, and other modern treatments. Soon, patients can manage their healthcare conditions. They can also take necessary measures to prevent escalation and deterioration of their health. Curative and reactive healthcare approaches will switch to preventive and proactive health management. Someday, people will be able to control their own lifestyle and future health, and that will bring a revolution.

In this journey, artificial intelligence or computational intelligence will play a pivotal role in improving the quality of services of healthcare systems, and that will bring a better coordination of care. Intelligent health will be the potential solution to keep up with the escalating increase of healthcare cost. Huge amounts of existing data in the healthcare sector can be managed with the tools of intelligent systems like machine learning, meta‐heuristic algorithms, big data, deep learning, internet‐of‐things (IoT), etc. It will be easy and faster for the surgeons, hospital, medical, and emergency staff to find the probable treatment or drug for rare diseases. Innovations of the intelligent systems in the healthcare arena may help society by reducing the cost and time of medical treatments; concrete solutions for a particular disease can be easily found.

This volume, comprising eight well‐versed chapters (apart from the introductory and concluding chapters), will entice the readers to engage with major emerging trends in technology that are supporting the advancement of the medical image analysis with the help of artificial intelligence and computational intelligence. This volume elaborates on the fundamentals and advancement of conventional approaches in the field of health care management. The scope of this volume also opens an arena in which researchers propose new approaches and review state‐of‐the‐art machine learning, computer vision, and soft computing techniques as well as relate the same to their applications in medical image analysis. The motivation of this volume is not only to put forward new ideas in technology innovation but also to analyse the effect of the same in the current context of medical healthcare.

Health care informatics, also referred as biomedical or medical informatics, is an application of information engineering and management in the medical field. Health care fundamentally covers the management and employment of patient health care information. It is a multidisciplinary field that studies and pursues the effectual use of biomedical data, knowledge for scientific inquiry, information, problem solving, and decision making. Chapter 1 provides an overview of a few smart healthcare practices.

Lung cancer is a fatal form of cancer around the world. The American Lung Association reports an estimated five‐year survival rate in lung cancer patients of 18.6%. The statistics affirm that the survival rate is significantly lower than in other forms of cancer. However, the five‐year survival rate stands at 56% when the disease is diagnosed in a localized stage. Some cases do not appear to have symptoms until cancer has reached a later stage. The primary cause of concern is the low percent of early lung cancer detection, which is merely 16%. Lung cancer staging is a procedure associated with the disease's successful prognosis and formulation of an efficient treatment plan. Medical imaging techniques play a vital role in the diagnosis of lung cancer. Accuracy is crucial in treatment as lung cancer is influenced by internal and external factors or mistaken for other pulmonary diseases. The staging of cancer allows for the significant elimination of treatment failures. However, cancer staging is a dynamic process that involves multiple and frequent modifications to recognize organ features. The staging process requires a more robust and automated technique that can provide sensitive and unique input to improve the overall treatment process. Thus, artificial intelligence sub‐branches such as deep learning play a vital role in initiating such improvements for an efficient cancer staging process. Chapter 2 uncovers the potential of a deep learning model combined with positron emission tomography—computed tomography (PET‐CT) to develop a technique that identifies tumors with more precision. The proposed research will assist doctors in accurately measuring the tumor and identifying the stage of lung cancer that will determine further treatment and an exact prognosis.

Cyber‐physical attacks (CP attacks), originating in cyber space but damaging physical infrastructure, are a significant recent research focus. Such attacks have affected many cyber‐physical systems (CPSs) such as smart grids, intelligent transportation systems, and medical devices. In Chapter 3, the authors consider techniques for the detection and mitigation of CP attacks on medical devices. It is obvious that such attacks have immense safety implications. This work is based on formal methods, a class of mathematically founded techniques for the specification and verification of safety‐critical systems. The interaction of a cardiac pacemaker is discussed. Subsequently, the authors provide an overview of formal methods with particular emphasis on run‐time based approaches, which are ideal for the design of security monitors. Two recently developed approaches are illustrated that assist in the detection of attacks as well as mitigation.

Integrating heterogeneous omics data profiles, such as genomics, epigenomics, and transcriptomics may provide new insights into discovering some unknown genomic mechanisms involved in cancer and other related complex diseases. The alterations of multiple omics, including gene mutations, epigenetic changes, and gene regulation modifications, are responsible for tumor initiation and cancer progression. Most of the multi‐view data profiles contain a huge number of genes, many of which are redundant, noisy, and irrelevant. It is computationally impractical to use these massive data sets without any filtering of the feature set. High performance (deep) machine learning strategies now appear to be an essential tool to learn the hidden structure from the data. In Chapter 4, the authors have proposed a two‐step approach to systematically identify gene signatures from multi‐omics head and neck cancer data. First, an autoencoder‐based strategy is used to integrate gene expression and methylation data. From this, the features are extracted by using the information from the bottleneck layer of the autoencoder. The features represent the combined representation of the two omics profiles. Next, the features that stem from the integrated data are applied to learn another deep learning model called the capsule network. The coupling coefficients between primary and output capsules are also analysed to interpret the features captured by the capsules.

The last two decades have witnessed unprecedented advancements in computational techniques and artificial intelligence. These new developments are going to greatly impact biological data analysis for the health care system. In fact, the availability of large scale high‐throughput biomedical data sets offers a fertile ground for application of these AI‐based techniques in to extract valuable information that can be harnessed in the diagnosis and treatment of various diseases. Chapter 5 provides a comprehensive review of computational tools and online resources for high throughput analyses of biomedical data. It focuses on single‐cell RNA sequencing data, multi‐omics data integration, drug design with AI, medical imaging data analysis, and IoT. After providing a brief overview of the fundamental biological terms, a variety of research problems are described in the health care system and how various high throughput data can help solving them. Next, an in depth overview of machine learning techniques of computing and learning methods that can be used in a variety of sequencing data analyses is provided.

Cancer is one of the most devastating diseases worldwide. It affects nearly every household, although cancer types are prevalent in different geographical regions. One example is breast cancer, which is the most common type of cancer in women worldwide. Therefore, prevention strategies are needed to address this issue. Identifying risk factors of breast cancer is crucial since it allows physicians to acquaint them with the risks. Accordingly, physicians can recommend precautionary actions. In the first part of Chapter 6, the authors detail the discovery of significant rules for breast cancer patients, focusing on different ethnic groups. Predicting the risk of the occurrence of breast cancer is an essential issue for clinical oncologists. A reliable prediction will help oncologists and other clinicians in their decision‐making process and allow clinicians to choose the most reliable and evidence‐based treatment. In the second part of the chapter, a super learner or stacked ensemble technique is employed to the breast cancer data set obtained from the Breast Cancer Surveillance Consortium (BCSC) database. A comparison of the performance of the super learner and the individual base learners is conducted. The results of the first part of this study (rule extraction from breast cancer patients in distinct ethnic groups) found well‐known ethnic disparities in cancer prevalence. The experimental results revealed that the produced rules hold the highest confidence level. The crucial rules, which can be easily understood, are also interpreted.

Negative‐stain transmission electron microscopy (TEM) is considered a fundamental approach for virus detection and identification. In this context, Chapter 7 presents a new architecture, based on neuro‐rough hybridization, for the analysis of TEM images. It assumes that a specific local descriptor at a given scale may be relevant in classifying a particular pair of virus classes but may not be able to encapsulate the inherent characteristics of another pair of classes. Important features from class‐pair relevant descriptors are, therefore, first identified using the rough hypercuboid approach, and then discriminatory features are learned using the contrastive divergence algorithm of the restricted Boltzmann machine (RBM). Finally, a support vector machine (SVM) with a linear kernel is adopted to categorize the TEM images into one of the known virus classes. The proficiency of the proposed approach with respect to several state‐of‐the‐art methods was established on a publicly available, benchmark Virus data set.

Computer vision plays a substantial role in health care applications such as the diagnosis of diseases and planning for treatment. Brain tumors are severe conditions that may be deadly if not detected and treated early. In India, brain tumors occur in five to ten people per one lakh population (100 000 people). Deep learning is a category of artificial intelligence that does not require any human intervention to learn the features. Deep learning algorithms learn the features of images on their own and are capable of learning more complex features from the images. While characterizing the deep neural networks, the selection of optimizers plays a vital role. Optimizers are used to minimize the loss function by varying the weights and learning rate attributes of the neural network. Optimization algorithms are essential for producing more accurate results by reducing the loss function of the neural network. In Chapter 8, the authors have analyzed popular optimizers such as sgd, adam, rmsprop, adagrad, adadelta, adamax, and nadam used with artificial neural network systems in the proposed work. Two models, a simple artificial neural network (ANN) model and a convolutional neural network (CNN) model, have been considered. Each optimizer is executed with these two models to classify abnormal slices from magnetic resonance imaging (MRI) of human brain scans. The BraTS2013 and WBA data sets were used for training and testing the models. The accuracies of every model were recorded to analyse the optimizer's performance.

In Chapter 9, a machine learning approach is proposed to predict whether the given brain MRI scans are normal or abnormal. This prediction is needed for treatment planning and diagnosis. The proposed method makes use of the bilateral symmetric nature of the human brain by splitting it into the left and right hemispheres (LHS and RHS) to extract the feature differences between the hemispheres. A feature set of 763 x 39 dimensions are created as the input for the classification model. Among these 39 features, 16 were selected by the Pearson's correlation coefficient to have correlation value greater than 0.3. To train the model, six tumor volumes from the BraTS2013 and two normal volumes from the IBSR‐18 data sets were used. For testing the model, 11 tumor volumes from the BraTS2013 and two normal volumes from the IBSR‐18 data sets were used. The k‐nearest neighbourhood (KNN) model was trained using the training data and the prediction done on the test data. A stratified k‐fold cross‐validation was used to validate the proposed model. The proposed model was analysed in terms of false alarm (FA), missed alarm (MA), and accuracy (ACC) for performance. The results showed that the proposed model yielded a 98 and 95.6% accuracy on the validation and testing data, respectively.

Chapter 10 draws a line of conclusion on the future aspects of healthcare informatics while stressing the need for the effective management of healthcare resources.

This volume will benefit several categories of students and researchers. At the student level, this volume can serve as a treatise/reference book for the special papers at the master's level aimed at inspiring future researchers. Newly inducted PhD aspirants would also find the contents of this volume useful as far as their compulsory coursework is concerned. At the researchers' level, those interested in interdisciplinary research would also benefit from the volume. After all, the enriched interdisciplinary contents of the volume will always be a subject of interest to the faculties, existing research communities, and new research aspirants from diverse disciplines of the concerned departments of premier institutes across the globe.

Cooch Behar, India	Sourav De
Ranchi, India	Rik Das
Bengaluru, India	Siddhartha Bhattacharyya
Kolkata, India	Ujjwal Maulik
December, 2021