This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The study is the first evaluation of a major ongoing national program that uses the classical experimental design of random assignment, measuring "what would have happened" by comparing people who entered Job Training Partnership Act (JTPA) programs with those who didn't. After background information on JTPA, chapters look at benefit-cost analyses; enrollment; program impacts on target groups; impacts on the earnings of subgroups; and policy implications of the findings. Distributed by University Press of America. Annotation copyright by Book News, Inc., Portland, OR
Across OECD countries, almost one in every five students does not reach a basic minimum level of skills. This book presents a series of policy recommendations for education systems to help all children succeed.
The extensive adoption of artificial intelligence (AI) that is presently taking place in the medical industry is expected to have a significant positive impact on many facets of patient care, diagnostics, therapies, and healthcare management. The Most Important Applications of Artificial Intelligence in Healthcare X-rays, magnetic resonance imaging (MRI), and computed tomography (CT) scans are only some examples of the several kinds of medical images that can be examined by artificial intelligence algorithms to assist radiologists in making diagnosis. Deep learning techniques enable artificial intelligence systems to discover patterns and irregularities with a high degree of accuracy, which can help in the early detection of diseases like cancer as well as improve the efficiency of visual interpretation. By analyzing patient records, data, and symptoms, AI can assist medical professionals in making accurate diagnoses. Using machine learning algorithms, intricate relationships hidden within massive datasets can be unearthed, making it possible to arrive at more accurate diagnoses. AI-powered decision support systems can help practitioners with therapy suggestions by using the most recent findings from medical research as well as data particular to the individual patient. The medication development process can be sped up with the assistance of AI by sorting through mountains of data derived from many sources such as biological databases, scientific articles, and the results of clinical trials. By utilizing machine learning models to predict the efficacy and side effects of potential drug candidates, the amount of time and money needed to develop novel medicines can be cut in half. This is a significant improvement. Patients can benefit from chatbots and virtual assistants powered by AI in a number of ways, including receiving individualized health information, having their questions answered, and receiving recommendations for the next steps to take. These kinds of tools can assist medical professionals in prioritizing patients in accordance with their symptoms, recommending self-care practices, and scheduling appointments.
People want to live a healthy and happy later life. A large body of literature shows the close association between health status and happiness and between health and active engagement (in work, exercise, and social and religious activities). However, the causation between the two can run both ways, and it is difficult to determine the causal effect with cross-sectional data. Various authors have shown the significant influence of socioeconomic factors and human needs on older people’s health status and happiness. A better understanding of the factors affecting healthy and happy aging is essential for policymaking to improve the well-being of older people. The availability of data from HRS-family studies in several Asian countries (CHARLS in China, LASI in India, JSTAR in Japan, KLoSA in Korea, IFLS in Indonesia, HART in Thailand, MARS in Malaysia, and Longitudinal Study of Ageing and Health in Viet Nam) (see Gateway to Global Aging Data) provides an excellent opportunity for researchers to examine factors affecting health and happiness among older adults within and across Asian countries. This research topic aims to gather papers that investigate the socioeconomic, attitudinal, and behavioural factors affecting the health status and happiness/life satisfaction of older adults in Asia. The dependent variables may include physical health, mental health, disability (ADL/IADL), cognitive functioning), self-rated health, health expenditure, feeling of happiness and life satisfaction. The independent variables may be age, gender, marital status, place of residence, educational level, active engagement (work, exercise, social and religious activities), family and social relationship and support, outlook in life, smoking, drinking, and access to and utilization of healthcare services, etc. Manuscripts can be based on individual countries or cross-country analysis, preferably using the panel data to establish the causal effects of the independent variables on the dependent variables.
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Departments of Labor, and Health, Education, and Welfare, and Related Agencies
