Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

A survey on deep learning in medical image analysis

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.

https://www.cs.umb.edu/%7Eding/papers/TKDE2013.pdf

Data mining with big data

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

/pdf/unobtrusive-sensing-and-wearable-devices-for-health-wgdblf1mea.pdf

Unobtrusive Sensing and Wearable Devices for Health Informatics

The development of the wireless body area sensor network (BASN) is imperative for modern telemedicine and m-health, but security remains a formidable challenge yet to be resolved. As nodes of BASN are expected to be interconnected on or in the human body, the body itself can form an inherently secure communication pathway that is unavailable to all other kinds of wireless networks. This article explores the use of this conduit in the security mechanism of BASN; that is, by a biometrics approach that uses an intrinsic characteristic of the human body as the authentication identity or the means of securing the distribution of a cipher key to secure inter-BASN communications. The method was tested on 99 subjects with 838 segments of simultaneous recordings of electrocardiogram and photoplethysmogram. By using the interpulse interval (IPI) as the biometric trait, the system achieved a minimum half total error rate of 2.58 percent when the IPIs measured from signals, which were sampled at 1000 Hz, were coded into 128-bit binary sequences. The study opens up a few key issues for future investigation, including compensation schemes for the asynchrony of different channels, coding schemes, and other suitable biometric traits.

A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Big Data for Health

The current blood pressure (BP) measurement devices are mostly built on the principle of auscultation, oscillometry or tonometry, all of which use an inflatable cuff to occlude or unload the artery. The need of a cuff in these devices limits the further reduction in size and power consumption, and restricts the frequency and ease of their usage. Therefore, this study aims to develop a cuff-less and noninvasive technique for measuring BP by pulse transit time. The technique was evaluated on 85 subjects, aged 57plusmn29 yrs., including 36 males and 39 hypertensives, over an average period of 6.4 wks. A total of 999 pairs of systolic BP (SBP) and diastolic BP (DBP) estimations were made. The average of BP readings reported by an experienced registered nurse and those obtained from a clinically approved automatic BP meter was used as reference. It is found that the estimated SBP and DBP differed from the reference BP by 0.6plusmn9.8 mmHg and 0.9plusmn5.6 mmHg respectively. When compared to the AAMI requirement (5plusmn8 mmHg for both SBP and DBP estimation), the results suggest that the cuff-less technology has great potential to be developed into wearable devices that are useful in self BP monitoring for home healthcare and eventually in clinical diagnosis

Cuff-less and Noninvasive Measurements of Arterial Blood Pressure by Pulse Transit Time

Noninvasive and real-time cuffless blood pressure (BP) measurement realizes the idea of unobtrusive and continuous BP monitoring which is essential for diagnosis and prevention of cardiovascular diseases associated with hypertension. In this paper, a wearable sensor patch system that integrates flexible piezoresistive sensor (FPS) and epidermal electrocardiogram (ECG) sensors for cuffless BP measurement is presented. By developing parametric models on the FPS sensing mechanism and optimizing operational conditions, a highly stable epidermal pulse monitoring method is established and beat-to-beat BP measurement from the ECG and epidermal pulse signals is demonstrated. In particular, this study highlights the compromise between sensor sensitivity and signal stability. As compared with the current optical-based cuffless BP measurement devices, the sensing patch requires much lower power consumption (3 nW) and is capable of detecting subtle physiological signal variations, e.g., pre and postexercises, thus providing a promising solution for low-power, real-time, and home-based BP monitoring.

Carmen C. Y. Poon

Papers

Unobtrusive Sensing and Wearable Devices for Health Informatics

A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health

Big Data for Health

Cuff-less and Noninvasive Measurements of Arterial Blood Pressure by Pulse Transit Time

Flexible Piezoresistive Sensor Patch Enabling Ultralow Power Cuffless Blood Pressure Measurement