Practical Healthcare Statistics With Examples In Python And R
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Practical Healthcare Statistics with Examples in Python and R
Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis. The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data. These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.
The Transformative Power of AI Applications in Healthcare
Author: Ayman Elmassarawy
language: en
Publisher: Ayman Elmassarawy
Release Date: 2025-01-24
Artificial intelligence (AI) is revolutionizing the healthcare industry, reshaping how medicine is practiced and how care is delivered. The Transformative Power of AI Applications in Healthcare delves into the dynamic intersection of AI and medicine, offering readers a profound understanding of the innovations driving change and the challenges that accompany them. With its compelling blend of technological insight and real-world applications, this book serves as an essential resource for medical professionals, technologists, policymakers, and curious minds. Key Themes and Innovations The book begins by laying the foundational concepts of AI in healthcare, tracing its evolution from early data visualization techniques to today’s advanced machine learning algorithms. Readers are introduced to the transformative potential of predictive analytics, where wearable devices, early warning systems, and disease modeling enable the detection and prevention of illnesses long before symptoms manifest. Revolutionizing Diagnostics: One of the standout themes is AI’s role in enhancing diagnostic accuracy and efficiency. Chapters illustrate how deep learning algorithms, such as those used in radiology and pathology, are empowering clinicians with faster, more reliable diagnoses. Case studies, including AI’s application in detecting diabetic retinopathy and breast cancer, reveal its ability to expand diagnostic capabilities, particularly in underserved regions. Personalized Medicine and Genomics: The exploration of genomics and pharmacogenomics highlights how AI is personalizing care to unprecedented levels. The book unpacks the shift from generic treatment protocols to precision therapies tailored to individual genetic profiles, citing examples like targeted cancer treatments and optimized drug dosing strategies. Surgical Innovations and Robotics: AI’s integration into robotic surgery systems is revolutionizing operating rooms worldwide. From minimally invasive procedures to real-time decision-making tools, readers learn how AI enhances surgical precision, reduces risks, and expands access to complex surgeries. The potential for remote surgeries and autonomous systems is also explored, showcasing the far-reaching impact of this technology. Public Health and Crisis Management: The role of AI in addressing global health crises takes center stage, with detailed discussions on how platforms like BlueDot and HealthMap provided early warnings during the COVID-19 pandemic. The book also examines AI’s contributions to vaccine development, resource allocation, and public health strategies, highlighting its transformative role in safeguarding global populations. Ethical Considerations and Challenges The book does not shy away from critical discussions on the ethical implications of AI. Topics such as algorithmic bias, data privacy, and equitable access to AI-driven innovations are examined with rigor. Readers are invited to consider how these challenges can be addressed through transparent, inclusive practices that prioritize fairness and human welfare. Vision for the Future Looking ahead, the book speculates on emerging frontiers of AI in healthcare. From mental health applications and autonomous diagnostics to advancements in bioprinting and chronic disease management, the potential for future breakthroughs is boundless. The narrative challenges readers to envision a healthcare system where AI seamlessly integrates with human expertise to deliver smarter, more compassionate care. Why This Book Matters At its core, The Transformative Power of AI Applications in Healthcare is not just about technology—it’s about the human impact. By offering an in-depth look at the advancements and ethical dilemmas surrounding AI, the book equips readers with the knowledge and inspiration to participate in shaping the future of medicine. It is both a roadmap and a call to action, urging readers to engage with the transformative power of AI and to champion its responsible implementation. This book is an indispensable guide for anyone interested in understanding how AI is reshaping healthcare. It illuminates the path forward, bridging the gap between innovation and humanity, and fostering a vision of a healthcare system that is smarter, more equitable, and profoundly impactful.
Practical Time Series Analysis
Author: Aileen Nielsen
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2019-09-20
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance