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Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc.

This book examines the nature of medical knowledge, how it is obtained, and how it can be used for decision support. It provides complete coverage of computational approaches to clinical decision-making. Chapters discuss data integration into healthcare information systems and delivery to point of care for providers, as well as facilitation of direct to consumer access. A case study section highlights critical lessons learned, while another portion of the work examines biostatistical methods including data mining, predictive modelling, and analysis. This book additionally addresses organizational, technical, and business challenges in order to successfully implement a computer-aided decision-making support system in healthcare delivery.

This report uses a comprehensive framework for studying health risks that was developed for the World Health Report 2002, which presented estimates for the year 2000. The report provides an update for the year 2004 for 24 global risk factors. It uses updated information from WHO programs and scientific studies for both exposure data and the causal associations of risk exposure to disease and injury outcomes. The burden of disease attributable to risk factors is measured in terms of lost years of healthy life using the metric of the disability-adjusted life year (DALY). The DALY combines years of life lost due to premature death with years of healthy life lost due to illness and disability. Health risks are in transition: populations are ageing owing to successes against infectious diseases; at the same time, patterns of physical activity and food, alcohol and tobacco consumption are changing. Low- and middle-income countries now face a double burden of increasing chronic, noncommunicable conditions, as well as the communicable diseases that traditionally affect the poor.

In order to alleviate poverty, people-oriented projects (those dealing with activities such as institutional development, health, family planning, education, and rural development) must be well-targeted towards the most vulnerable groups. However, official 'top-down' information is incapable of identifying, prioritising and 'marking out' these groups at a local level, and the result is too often an unfair, inefficient and ineffective allocation and use of the resources of social programmes. The author argues that a 'process approach' is often necessary to geneate relevant knowledge about local needs, especially in poor urban and heterogeneous areas. Such an approach fosters flexibility and adaptability to the local context. The book analyses the whole approach to information handling which was developed in the Brazilian project. The different phases are analysed; the collection and processing of data and the construction of an information system to be used for decision-making.

Several guidelines on cost-effectiveness analysis (CEA) already exist. There are two reasons for producing another set. The first is that traditional or ''incremental'' CEA ignores the question of whether the current mix of interventions represents an efficient use of resources. Secondly, the resources required to evaluate the large number of interventions required to use CEA to identify opportunities to enhance efficiency are prohibitive. The approach of Generalized CEA proposed in this Guide seeks to provide analysts with a method of assessing whether the current as well as proposed mix of interventions is efficient. It also seeks to maximize the generalizability of results across settings. The Guide, in Part I, begins with a brief description of Generalized CEA and how it relates to the two questions raised above. It then considers issues relating to study design, estimating costs, assessing health effects, discounting, uncertainty and sensitivity analysis, and reporting results. Detailed discussions of selected technical issues and applications are provided in a series of background papers, originally published in journals, but included in this book for easy reference in Part II. The Guide and these papers are written in the context of the work of WHO-CHOICE: CHOosing Interventions that are Cost-Effective. WHO-CHOICE is assembling regional databases on the costs, impact on population health and cost-effectiveness of key health interventions using standardized methodology and tools. WHO-CHOICE tools on costing (CostIt©), population effectiveness modelling (PopMod©) and probabilistic uncertainty analysis (MCLeague©) are included in the accompanying compact disc.

Non-heart-beating donors (individuals whose deaths are determined by cessation of heart and respiratory function rather than loss of whole brain function) could potentially be of major importance in reducing the gap between the demand for and available supply of organs for transplantation. Prompted by questions concerning the medical management of such donors-specifically, whether interventions undertaken to enhance the supply and quality of potentially transplantable organs (i.e. the use of anticoagulants and vasodilators) were in the best interests of the donor patient-the U.S. Department of Health and Human Services asked the Institute of Medicine to examine from scientific and ethical points of view \"alternative medical approaches that can be used to maximize the availability of organs from [a] donor [in an end-of-life situation] without violating prevailing ethical norms...\" This book examines transplantation supply and demand, historical and modern conceptions of non-heart-beating donors, and organ procurement organizations and transplant program policies, and contains recommendations concerning the principles and ethical issues surrounding the topic.

Praise for the First Edition \". . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists.\" -International Statistical Institute A new edition of the definitive guide to classical and modern methods of biostatistics Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data. Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Subsequent chapters present modern model-based approaches that include unconditional and conditional logistic regression; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its generalizations. The book now includes an introduction to mixed models with fixed and random effects as well as expanded methods for evaluation of sample size and power. Additional new topics featured in this Second Edition include: Establishing equivalence and non-inferiority Methods for the analysis of polychotomous and ordinal data, including matched data and the Kappa agreement index Multinomial logistic for polychotomous data and proportional odds models for ordinal data Negative binomial models for count data as an alternative to the Poisson model GEE models for the analysis of longitudinal repeated measures and multivariate observations Throughout the book, SAS is utilized to illustrate applications to numerous real-world examples and case studies. A related website features all the data used in examples and problem sets along with the author's SAS routines. Biostatistical Methods, Second Edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.

Country health information systems provide a rich source of data on the burden of diseasecaused by tuberculosis (TB) and the effectiveness of programmatic efforts to reduce thisburden both of which are crucial for public health action. However the available dataare often underused or not used at all. At least in part this may reflect the absence ofclear guidance on recommended approaches to the analysis of such data. This handbookis designed to address this gap through detailed practical examples of the analysis of TBsurveillance data in particular TB notification data data from surveillance of anti-TB drugresistance and mortality data compiled in national vital registration systems. It starts fromthe most basic kinds of analyses and progresses to the description of more challengingtopics such as the estimation of disease burden using multiple sources of evidence includingdata from special surveys.

目標：失眠是常見的主觀症狀，會影響生活品質及身體健康，本研究目的為運用C4.5決策樹工具，以找出影響失眠問題關鍵重要因素，以供相關人員參考。方法：本研究收集2015年1月至12月間到台中市某區域醫院接受全身健康檢查之20歲以上之民眾為樣本個案，共收集有效樣本1,223人，其中，男性佔756人（61.8%）。研究使用結構性問卷，有人口統計基本資料、失眠症狀、生活型態、憂鬱及泛焦慮症狀等資訊。本研究以SPSS 20.0分析資料，憂鬱、焦慮量表及總量表之內在一致性（Cronbach's α）值分別為0.78、0.78及0.86。本研究進一步使用Weka 3.8.1之「C4.5決策樹」以產生規則以了解哪些因素的組合可能容易導致失眠症狀。結果：入睡困難、夜眠中斷、早醒、醒後疲倦、嚴重失眠問題等各種睡眠障礙因素之間的彼此相關性高；男性與入睡困難、夜眠中斷問題的相關性比女性高；睡前喝水、睡前喝酒、飲咖啡習慣、飲茶習慣皆與嚴重失眠問題正相關；安眠藥與各種睡眠障礙正相關；民眾擁有職業可能會改善入睡困難與夜眠中斷的問題；家庭壓力、工作壓力與其他壓力皆可能會產生睡眠障礙；運動習性的養成，不管是增加運動頻率或是增加運動量皆有助於部份民眾降低失眠障礙；睡前喝酒在短期內有助於克服睡眠問題；戒除飲咖啡習慣對部份民眾將有助於減緩失眠障礙；服用藥物對身體除了可能會產生各種副作用外，亦可能會對部分民眾造成失眠問題；抽煙習慣亦會對部分民眾產生睡眠障礙，擁有職業可降低部份民眾的失眠問題。結論：本研究使用決策樹針對不同類型的民眾進行統計分析以產生規則，發現養成運動習性、少抽煙、少飲咖啡、少服藥物、從事職業活動、睡前避免喝水等，有助於部份民眾改善失眠問題。