Computer-aided Trauma Decision Making Using Machine Learning and Signal Processing PDF Download
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Author: Soo-Yeon Ji
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages :
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Book Description
Over the last 20 years, much work has focused on computer-aided clinical decision support systems due to a rapid increase in the need for management and processing of medical knowledge. Among all fields of medicine, trauma care has the highest need for proper information management due to the high prevalence of complex, life-threatening injuries. In particular, hemorrhage, which is encountered in most traumatic injuries, is a dominant factor in determining survival in both civilian and military settings. This complication can be better managed using a more in-depth analysis of patient information. Trauma physicians must make precise and rapid decisions, while considering a large number of patient variables and dealing with stressful environments. The ability of a computer-aided decision making system to rapidly analyze a patient2s condition can enable physicians to make more accurate decisions and thereby significantly improve the quality of care provided to patients. The first part of this study is focused on classification of highly complex databases using a hierarchical method which combines two complementary techniques: logistic regression and machine learning. This method, hereafter referred to as Classification Using Significant Features (CUSF), includes a statistical process to select the most significant variables from the correlated database. Then a machine learning algorithm is used to identify the data into classes using only the significant variables. As the main application addressed by CUSF, a set of computer-assisted rule-based trauma decision making system are designed. Computer aided decision-making system not only provides vital assistance for physicians in making fast and accurate decisions, proposed decisions are supported by transparent reasoning, but also can confirm a physicians' current knowledge, enabling them to detect complex patterns and information which may reveal new knowledge not easily visible to the human eyes. The second part of this study proposes an algorithm based on a set of novel wavelet features to analyze physiological signals, such as Electrocardiograms (ECGs) that can provide invaluable information typically invisible to human eyes. These wavelet-based method, hereafter referred to as Signal Analysis Based on Wavelet-Extracted Features (SABWEF), extracts information that can be used to detect and analyze complex patterns that other methods such as Fourier cannot deal with. For instance, SABWEF can evaluate the severity of hemorrhagic shock (HS) from ECG, while the traditional technique of applying power spectrum density (PSD) and fractal dimension (FD) cannot distinguish between the ECG patterns of patients with HS (i.e. blood loss), and those of subjects undergoing physical activity. In this study, as the main application of SABWEF, ECG is analyzed to distinguish between HS and physical activity, and show that SABWEF can be used in both civilian and military settings to detect HS and its extent. This is the first reported use of an ECG analysis method to classify blood volume loss. SABWEF has the capability to rapidly determine the degree of volume loss from hemorrhage, providing the chance for more rapid remote triage and decision making.
Author: Soo-Yeon Ji
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages :
Get Book Here
Book Description
Over the last 20 years, much work has focused on computer-aided clinical decision support systems due to a rapid increase in the need for management and processing of medical knowledge. Among all fields of medicine, trauma care has the highest need for proper information management due to the high prevalence of complex, life-threatening injuries. In particular, hemorrhage, which is encountered in most traumatic injuries, is a dominant factor in determining survival in both civilian and military settings. This complication can be better managed using a more in-depth analysis of patient information. Trauma physicians must make precise and rapid decisions, while considering a large number of patient variables and dealing with stressful environments. The ability of a computer-aided decision making system to rapidly analyze a patient2s condition can enable physicians to make more accurate decisions and thereby significantly improve the quality of care provided to patients. The first part of this study is focused on classification of highly complex databases using a hierarchical method which combines two complementary techniques: logistic regression and machine learning. This method, hereafter referred to as Classification Using Significant Features (CUSF), includes a statistical process to select the most significant variables from the correlated database. Then a machine learning algorithm is used to identify the data into classes using only the significant variables. As the main application addressed by CUSF, a set of computer-assisted rule-based trauma decision making system are designed. Computer aided decision-making system not only provides vital assistance for physicians in making fast and accurate decisions, proposed decisions are supported by transparent reasoning, but also can confirm a physicians' current knowledge, enabling them to detect complex patterns and information which may reveal new knowledge not easily visible to the human eyes. The second part of this study proposes an algorithm based on a set of novel wavelet features to analyze physiological signals, such as Electrocardiograms (ECGs) that can provide invaluable information typically invisible to human eyes. These wavelet-based method, hereafter referred to as Signal Analysis Based on Wavelet-Extracted Features (SABWEF), extracts information that can be used to detect and analyze complex patterns that other methods such as Fourier cannot deal with. For instance, SABWEF can evaluate the severity of hemorrhagic shock (HS) from ECG, while the traditional technique of applying power spectrum density (PSD) and fractal dimension (FD) cannot distinguish between the ECG patterns of patients with HS (i.e. blood loss), and those of subjects undergoing physical activity. In this study, as the main application of SABWEF, ECG is analyzed to distinguish between HS and physical activity, and show that SABWEF can be used in both civilian and military settings to detect HS and its extent. This is the first reported use of an ECG analysis method to classify blood volume loss. SABWEF has the capability to rapidly determine the degree of volume loss from hemorrhage, providing the chance for more rapid remote triage and decision making.
Author: Ervin Sejdic
Publisher: CRC Press
ISBN: 149877346X
Category : Medical
Languages : en
Pages : 624
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Book Description
Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.
Author: Sumeet Dua
Publisher: Springer Science & Business Media
ISBN: 3642400175
Category : Technology & Engineering
Languages : en
Pages : 334
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Book Description
The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.
Author: Andreas Holzinger
Publisher: Springer
ISBN: 3319162268
Category : Medical
Languages : en
Pages : 283
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Book Description
Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems. The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning. The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.
Author: Anitha S. Pillai
Publisher: Academic Press
ISBN: 0323886264
Category : Science
Languages : en
Pages : 356
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Book Description
Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer’s disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer’s Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy. Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks. Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer’s Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders
Author: Tyler John Loftus
Publisher: Frontiers Media SA
ISBN: 2832533256
Category : Medical
Languages : en
Pages : 121
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Book Description
Author: Rafik A. Aliev
Publisher: Springer Nature
ISBN: 3030352498
Category : Technology & Engineering
Languages : en
Pages : 997
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Book Description
This book presents the proceedings of the 10th Conference on Theory and Applications of Soft Computing, Computing with Words and Perceptions, ICSCCW 2019, held in Prague, Czech Republic, on August 27–28, 2019. It includes contributions from diverse areas of soft computing and computing with words, such as uncertain computation, decision-making under imperfect information, neuro-fuzzy approaches, deep learning, natural language processing, and others. The topics of the papers include theory and applications of soft computing, information granulation, computing with words, computing with perceptions, image processing with soft computing, probabilistic reasoning, intelligent control, machine learning, fuzzy logic in data analytics and data mining, evolutionary computing, chaotic systems, soft computing in business, economics and finance, fuzzy logic and soft computing in earth sciences, fuzzy logic and soft computing in engineering, fuzzy logic and soft computing in material sciences, soft computing in medicine, biomedical engineering, and pharmaceutical sciences. Showcasing new ideas in the field of theories of soft computing and computing with words and their applications in economics, business, industry, education, medicine, earth sciences, and other fields, it promotes the development and implementation of these paradigms in various real-world contexts. This book is a useful guide for academics, practitioners and graduates.
Author:
Publisher:
ISBN:
Category : Medicine
Languages : en
Pages : 1060
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Book Description
Author: Juri Yanase
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 51
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Book Description
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Author: Yurong Luo
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages :
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Book Description
As a beneficial component with critical impact, computer-aided decision making systems have infiltrated many fields, such as economics, medicine, architecture and agriculture. The latent capabilities for facilitating human work propel high-speed development of such systems. Effective decisions provided by such systems greatly reduce the expense of labor, energy, budget, etc. The computer-aided decision making system for traumatic injuries is one type of such systems that supplies suggestive opinions when dealing with the injuries resulted from accidents, battle, or illness. The functions may involve judging the type of illness, allocating the wounded according to battle injuries, deciding the severity of symptoms for illness or injuries, managing the resources in the context of traumatic events, etc. The proposed computer-aided decision making system aims at estimating the severity of blood volume loss. Specifically speaking, accompanying many traumatic injuries, severe hemorrhage, a potentially life-threatening condition that requires immediate treatment, is a significant loss of blood volume in process resulting in decreased blood and oxygen perfusion of vital organs. Hemorrhage and blood loss can occur in different levels such as mild, moderate, or severe. Our proposed system will assist physicians by estimating information such as the severity of blood volume loss and hemorrhage, so that timely measures can be taken to not only save lives but also reduce the long-term complications as well as the cost caused by unmatched operations and treatments. The general framework of the proposed research contains three tasks and many novel and transformative concepts are integrated into the system. First is the preprocessing of the raw signals. In this stage, adaptive filtering is adopted and customized to filter noise, and two detection algorithms (QRS complex detection and Systolic/Diastolic wave detection) are designed. The second process is to extract features. The proposed system combines features from time domain, frequency domain, nonlinear analysis, and multi-model analysis to better represent the patterns when hemorrhage happens. Third, a machine learning algorithm is designed for classification of patterns. A novel machine learning algorithm, as a new version of error correcting output code (ECOC), is designed and investigated for high accuracy and real-time decision making. The features and characteristics of this machine learning method are essential for the proposed computer-aided trauma decision making system. The proposed system is tested agasint Lower Body Negative Pressure (LBNP) dataset, and the results indicate the accuracy and reliability of the proposed system.
