St. Peter's Hospital Director Lee, Choon Sung publishes ‘Getting Started with Bayesian Statistics’
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Gangnam St. Peter's Hospital 26-06-15 15:31Main content
A practicing spine specialist has published an introductory book on Bayesian statistics, the discipline that treats uncertain judgment mathematically.
Dr. Lee, Choon Sung, Director of the Spine Center at St. Peter's Hospital (President Yoon, Kangjun), a general hospital specializing in spine, joint, and cerebro-cardiovascular care, has published 'Getting Started with Bayesian Statistics', an introductory book covering the basic concepts of Bayesian statistics.
Bayesian statistics is a method of statistical analysis that corrects earlier judgments and improves the accuracy of predictions as data accumulates. Because it continuously incorporates new information and allows statistical inference to be updated flexibly, it is useful for dealing mathematically with real-world decision-making problems that involve a high degree of uncertainty.
Bayesian statistics is in fact used at the core of data-driven advanced technologies today, including AI and machine learning. A range of IT companies apply Bayesian statistics to search algorithms and data-driven models. In clinical settings, too, it is increasingly being applied in areas such as clinical trial design, prediction of treatment risk for individual patients, estimation of diagnostic accuracy and prevalence, and analysis of drug side effects.
Director Lee, Choon Sung first encountered Bayesian statistics ten years ago while at Asan Medical Center, and having found points of contact with medical judgment, he took a keen interest and has studied it steadily ever since. In clinical practice, rather than diagnosing a patient from a single test, physicians often repeat a process of judgment that improves treatment accuracy by combining the patient's symptoms, past medical history, risk factors and response to treatment. Director Lee, Choon Sung saw that this process has a structure similar to Bayesian statistical thinking, and was struck by the fact that clinical judgment can be expressed in precise probabilistic language.
This book is an introductory primer that explains Bayesian statistics in a way non-specialists can easily understand. Having struggled a great deal himself as someone without a background in statistics, the author focused on setting out the core concepts simply, so that even readers encountering Bayesian statistics for the first time can approach it without difficulty. It covers a broad range of related material in particular — the history of Bayesian statistics, Bayesian statistical thinking, and how it is applied in clinical medicine — so that readers can take in both the concepts and their applications and understand them as a whole.
In medical research and clinical practice, conventional frequentist statistics is still the more widely used approach. A representative example is the use of the 'p-value' to judge the statistical significance of research results.
Recently, however, as the population ages rapidly, the number of older patients and patients with multiple conditions is increasing, and the age of onset of diseases once regarded as illnesses of old age is also falling, so the importance of more flexible and comprehensive medical judgment is growing as well. Amid these changes in the healthcare environment, Bayesian statistical thinking can serve as a useful approach for making systematic and precise clinical judgments. In his book, Director Lee, Choon Sung revisits such frequentist statistics-based research from the perspective of Bayesian statistics and also offers a new way of looking at medical data and clinical interpretation.
Dr. Lee, Choon Sung said, "Bayesian statistics is a tool that helps you make better decisions and judgments even under uncertain conditions and with limited information, and it can be usefully applied to solving a wide range of real-world problems, including those in clinical practice," adding, "I hope this book will allow more people to understand the basic concepts of Bayesian statistics and encounter a new perspective on statistical thinking."
Source: Medworld News (http://www.medworld.co.kr)
Dr. Lee, Choon Sung, Director of the Spine Center at St. Peter's Hospital (President Yoon, Kangjun), a general hospital specializing in spine, joint, and cerebro-cardiovascular care, has published 'Getting Started with Bayesian Statistics', an introductory book covering the basic concepts of Bayesian statistics.
Bayesian statistics is a method of statistical analysis that corrects earlier judgments and improves the accuracy of predictions as data accumulates. Because it continuously incorporates new information and allows statistical inference to be updated flexibly, it is useful for dealing mathematically with real-world decision-making problems that involve a high degree of uncertainty.
Bayesian statistics is in fact used at the core of data-driven advanced technologies today, including AI and machine learning. A range of IT companies apply Bayesian statistics to search algorithms and data-driven models. In clinical settings, too, it is increasingly being applied in areas such as clinical trial design, prediction of treatment risk for individual patients, estimation of diagnostic accuracy and prevalence, and analysis of drug side effects.
Director Lee, Choon Sung first encountered Bayesian statistics ten years ago while at Asan Medical Center, and having found points of contact with medical judgment, he took a keen interest and has studied it steadily ever since. In clinical practice, rather than diagnosing a patient from a single test, physicians often repeat a process of judgment that improves treatment accuracy by combining the patient's symptoms, past medical history, risk factors and response to treatment. Director Lee, Choon Sung saw that this process has a structure similar to Bayesian statistical thinking, and was struck by the fact that clinical judgment can be expressed in precise probabilistic language.
This book is an introductory primer that explains Bayesian statistics in a way non-specialists can easily understand. Having struggled a great deal himself as someone without a background in statistics, the author focused on setting out the core concepts simply, so that even readers encountering Bayesian statistics for the first time can approach it without difficulty. It covers a broad range of related material in particular — the history of Bayesian statistics, Bayesian statistical thinking, and how it is applied in clinical medicine — so that readers can take in both the concepts and their applications and understand them as a whole.
In medical research and clinical practice, conventional frequentist statistics is still the more widely used approach. A representative example is the use of the 'p-value' to judge the statistical significance of research results.
Recently, however, as the population ages rapidly, the number of older patients and patients with multiple conditions is increasing, and the age of onset of diseases once regarded as illnesses of old age is also falling, so the importance of more flexible and comprehensive medical judgment is growing as well. Amid these changes in the healthcare environment, Bayesian statistical thinking can serve as a useful approach for making systematic and precise clinical judgments. In his book, Director Lee, Choon Sung revisits such frequentist statistics-based research from the perspective of Bayesian statistics and also offers a new way of looking at medical data and clinical interpretation.
Dr. Lee, Choon Sung said, "Bayesian statistics is a tool that helps you make better decisions and judgments even under uncertain conditions and with limited information, and it can be usefully applied to solving a wide range of real-world problems, including those in clinical practice," adding, "I hope this book will allow more people to understand the basic concepts of Bayesian statistics and encounter a new perspective on statistical thinking."
Source: Medworld News (http://www.medworld.co.kr)