Logic and Critical Thinking in the Biomedical Sciences: Volume I: Deductions Based upon Simple Observations provides biomedical students and scientists with a repertoire of deductive non-mathematical methods, that helps them draw useful inferences from their own data. The past decade has witnessed a huge increase in the number of books describing advanced mathematical methods for data analysis; however, experienced scientists know that such methods lead to erroneous conclusions unless the researchers have a deep understanding of what the data is trying to say. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from the data, and to guide the subsequent implementation of robust, mathematical analyses.
The first volume, Deductions Based Upon Simple Observations, invites readers to linger over a collection of common observations, and to see what inferences can be drawn, when one applies a bit of deductive logic. It demonstrates that if we just think about what we observe, it is possible often to discover profound biomedical insights. It is also possible, in some cases, to use logical analysis to correct old misconceptions that were drawn hastily or without enough introspection.
This book is a valuable source for several members of biomedical field who need to understand better how to make sense of all the medical data available currently.
- Provides a serious and scientific based discussion on deductive methods in the biomedical sciences
- Discusses deduction with a linear and coherent narrative, in order to engage and guide the readers on a full understanding of such complex, but neglect topic
- Brings examples and case studies in a relaxed manner, intended to draw the reader's attention to general concepts, without dwelling excessively on details
2. Drawing inferences from microphotography
3. Inferences drawn from anatomy
4. Inferences drawn from the early steps of a temporal sequence
5. Finding relationships among biological entities
6. Drawing Inferences from classifications and ontologies
7. Discoveries made by reducing class noise
8. We should have known better: How a little thought could have corrected long-held misbeliefs
Jules J. Berman received two baccalaureate degrees from MIT; in Mathematics, and in Earth and Planetary Sciences. He holds a PhD from Temple University, and an MD, from the University of Miami. He was a graduate student researcher in the Fels Cancer Research Institute, at Temple University, and at the American Health Foundation in Valhalla, New York. His postdoctoral studies were completed at the US National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, DC. Dr. Berman served as Chief of Anatomic Pathology, Surgical Pathology, and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred to the US National Institutes of Health, as a Medical Officer, and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past president of the Association for Pathology Informatics, and the 2011 recipient of the Association's Lifetime Achievement Award. He has first-authored more than 100 journal articles and has written 18 science books. His most recent titles, published by Elsevier, include:
-Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 1st edition (2012) -Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information (2013) -Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases (2014) -Repurposing Legacy Data: Innovative Case Studies (2015) -Data Simplification: Taming Information with Open Source Tools (2016) -Precision Medicine and the Reinvention of Human Disease (2018) -Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition (2018) -Taxonomic Guide to Infectious Diseases: Understanding the Biologic Classes of Pathogenic Organisms, 2nd edition (2019)