This second edition of a pioneering technical work in biomedical informatics provides a very readable treatment of the deep computational ideas at the foundation of the field. Principles of Biomedical Informatics, 2nd Edition is radically reorganized to make it especially useable as a textbook for courses that move beyond the standard introductory material. It includes exercises at the end of each chapter, ideas for student projects, and a number of new topics, such as: • tree structured data, interval trees, and time-oriented medical data and their use • On Line Application Processing (OLAP), an old database idea that is only recently coming of age and finding surprising importance in biomedical informatics • a discussion of nursing knowledge and an example of encoding nursing advice in a rule-based system • X-ray physics and algorithms for cross-sectional medical image reconstruction, recognizing that this area was one of the most central to the origin of biomedical computing • an introduction to Markov processes, and • an outline of the elements of a hospital IT security program, focusing on fundamental ideas rather than specifics of system vulnerabilities or specific technologies.
It is simultaneously a unified description of the core research concept areas of biomedical data and knowledge representation, biomedical information access, biomedical decision-making, and information and technology use in biomedical contexts, and a pre-eminent teaching reference for the growing number of healthcare and computing professionals embracing computation in health-related fields.
As in the first edition, it includes many worked example programs in Common LISP, the most powerful and accessible modern language for advanced biomedical concept representation and manipulation.
The text also includes humor, history, and anecdotal material to balance the mathematically and computationally intensive development in many of the topic areas. The emphasis, as in the first edition, is on ideas and methods that are likely to be of lasting value, not just the popular topics of the day. Ira Kalet is Professor Emeritus of Radiation Oncology, and of Biomedical Informatics and Medical Education, at the University of Washington. Until retiring in 2011 he was also an Adjunct Professor in Computer Science and Engineering, and Biological Structure. From 2005 to 2010 he served as IT Security Director for the University of Washington School of Medicine and its major teaching hospitals. He has been a member of the American Medical Informatics Association since 1990, and an elected Fellow of the American College of Medical Informatics since 2011. His research interests include simulation systems for design of radiation treatment for cancer, software development methodology, and artificial intelligence applications to medicine, particularly expert systems, ontologies and modeling.
1. Principles of Biomedical Informatics - p. I 2. Cover Image - p. I 3. Principles of Biomedical Informatics - p. i 4. Title Page - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Dedication - p. v 8. Table of Contents - p. v 9. Foreword from the First Edition - p. xvii 10. Preface from the First Edition - p. xxi 11. List of Figures - p. xxxiii 12. List of Tables - p. xxxix 13. Chapter 1 Biomedical Data - p. 1 14. 1.1: The Nature and Representation of Biomedical Data - p. 5 15. 1.1.1: What Can Be Represented in a Computer? - p. 18 16. 1.1.2: Cells, DNA, Proteins, and the Genetic Code - p. 37 17. 1.1.3: Anatomy - p. 54 18. 1.1.4: Medical Laboratory Data - p. 56 19. 1.1.5: Medical Images - p. 63 20. 1.1.6: Metadata - p. 83 21. 1.2: Objects, Metadata, and Serialization - p. 87 22. 1.3: Database Concepts and Systems - p. 133 23. 1.4: Data Quality - p. 165 24. 1.5: Data, Information, and Knowledge - p. 169 25. 1.6: Summary - p. 173 26. 1.7: Exercises - p. 173 27. Chapter 2 Symbolic Biomedical Knowledge - p. 179 28. 2.1: Biomedical Theories and Computer Programs - p. 182 29. 2.2: Logic and Inference Systems - p. 187 30. 2.2.1: Predicate Calculus - p. 189 31. 2.2.2: Unsound Inference: Abduction and Induction - p. 218 32. 2.2.3: First Order Logic (FOL) - p. 219 33. 2.2.4: Rule-Based Programming for FOL - p. 230 34. 2.2.5: Example: Predicting Drug Interactions by Logical Inference - p. 237 35. 2.2.6: Truth Maintenance Systems - p. 242 36. 2.2.7: Electronic Medical Records and Guidelines - p. 254 37. 2.2.8: Example: Cancer Radiotherapy Planning - p. 255 38. 2.2.9: Example: Rule-Based Nursing Knowledge - p. 272 39. 2.2.10: Limitations of FOL - p. 277 40. 2.3: Frames, Semantic Nets, and Ontologies - p. 281 41. 2.4: Description Logics - p. 321 42. 2.5: Summary - p. 335 43. 2.6: Exercises - p. 337 44. Chapter 3 Probabilistic Biomedical Knowledge - p. 343 45. 3.1: Probability and Statistics - p. 344 46. 3.2: Bayes’ Rule, Bayes Nets, and Probabilistic Inference - p. 364 47. 3.3: Utility and Decision Modeling - p. 375 48. 3.4: Information Theory - p. 382 49. 3.5: Summary - p. 395 50. 3.6: Exercises - p. 395 51. Chapter 4 Biomedical Information Access - p. 397 52. 4.1: Information Retrieval Systems - p. 398 53. 4.2: Beyond Text Documents - p. 413 54. 4.3: Networks and Biomedical Data Communication - p. 415 55. 4.4: Summary - p. 476 56. 4.5: Exercises - p. 476 57. Chapter 5 Computational Models and Methods - p. 479 58. 5.1: Computing with Genes, Proteins, and Cells - p. 480 59. 5.2: Search - p. 496 60. 5.3: Natural Language Processing - p. 523 61. 5.4: State Machines and Dynamic Models - p. 525 62. 5.5: Stochastic Processes - p. 562 63. 5.6: Summary - p. 575 64. 5.7: Exercises - p. 576 65. Chapter 6 Biomedical Data, Knowledge, and Systems in Context - p. 579 66. Chapter 7 Epilogue - p. 603 67. Appendix A Notes on Lisp, Other Languages, and Resources - p. 607 68. Bibliography - p. 627 69. Index - p. 647
This second edition of a pioneering technical work in biomedical informatics provides a very readable treatment of the deep computational ideas at the foundation of the field. Principles of Biomedical Informatics, 2nd Edition is radically reorganized to make it especially useable as a textbook for courses that move beyond the standard introductory material. It includes exercises at the end of each chapter, ideas for student projects, and a number of new topics, such as: • tree structured data, interval trees, and time-oriented medical data and their use • On Line Application Processing (OLAP), an old database idea that is only recently coming of age and finding surprising importance in biomedical informatics • a discussion of nursing knowledge and an example of encoding nursing advice in a rule-based system • X-ray physics and algorithms for cross-sectional medical image reconstruction, recognizing that this area was one of the most central to the origin of biomedical computing • an introduction to Markov processes, and • an outline of the elements of a hospital IT security program, focusing on fundamental ideas rather than specifics of system vulnerabilities or specific technologies.
It is simultaneously a unified description of the core research concept areas of biomedical data and knowledge representation, biomedical information access, biomedical decision-making, and information and technology use in biomedical contexts, and a pre-eminent teaching reference for the growing number of healthcare and computing professionals embracing computation in health-related fields.
As in the first edition, it includes many worked example programs in Common LISP, the most powerful and accessible modern language for advanced biomedical concept representation and manipulation.
The text also includes humor, history, and anecdotal material to balance the mathematically and computationally intensive development in many of the topic areas. The emphasis, as in the first edition, is on ideas and methods that are likely to be of lasting value, not just the popular topics of the day. Ira Kalet is Professor Emeritus of Radiation Oncology, and of Biomedical Informatics and Medical Education, at the University of Washington. Until retiring in 2011 he was also an Adjunct Professor in Computer Science and Engineering, and Biological Structure. From 2005 to 2010 he served as IT Security Director for the University of Washington School of Medicine and its major teaching hospitals. He has been a member of the American Medical Informatics Association since 1990, and an elected Fellow of the American College of Medical Informatics since 2011. His research interests include simulation systems for design of radiation treatment for cancer, software development methodology, and artificial intelligence applications to medicine, particularly expert systems, ontologies and modeling.
1. Principles of Biomedical Informatics - p. I 2. Cover Image - p. I 3. Principles of Biomedical Informatics - p. i 4. Title Page - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Dedication - p. v 8. Table of Contents - p. v 9. Foreword from the First Edition - p. xvii 10. Preface from the First Edition - p. xxi 11. List of Figures - p. xxxiii 12. List of Tables - p. xxxix 13. Chapter 1 Biomedical Data - p. 1 14. 1.1: The Nature and Representation of Biomedical Data - p. 5 15. 1.1.1: What Can Be Represented in a Computer? - p. 18 16. 1.1.2: Cells, DNA, Proteins, and the Genetic Code - p. 37 17. 1.1.3: Anatomy - p. 54 18. 1.1.4: Medical Laboratory Data - p. 56 19. 1.1.5: Medical Images - p. 63 20. 1.1.6: Metadata - p. 83 21. 1.2: Objects, Metadata, and Serialization - p. 87 22. 1.3: Database Concepts and Systems - p. 133 23. 1.4: Data Quality - p. 165 24. 1.5: Data, Information, and Knowledge - p. 169 25. 1.6: Summary - p. 173 26. 1.7: Exercises - p. 173 27. Chapter 2 Symbolic Biomedical Knowledge - p. 179 28. 2.1: Biomedical Theories and Computer Programs - p. 182 29. 2.2: Logic and Inference Systems - p. 187 30. 2.2.1: Predicate Calculus - p. 189 31. 2.2.2: Unsound Inference: Abduction and Induction - p. 218 32. 2.2.3: First Order Logic (FOL) - p. 219 33. 2.2.4: Rule-Based Programming for FOL - p. 230 34. 2.2.5: Example: Predicting Drug Interactions by Logical Inference - p. 237 35. 2.2.6: Truth Maintenance Systems - p. 242 36. 2.2.7: Electronic Medical Records and Guidelines - p. 254 37. 2.2.8: Example: Cancer Radiotherapy Planning - p. 255 38. 2.2.9: Example: Rule-Based Nursing Knowledge - p. 272 39. 2.2.10: Limitations of FOL - p. 277 40. 2.3: Frames, Semantic Nets, and Ontologies - p. 281 41. 2.4: Description Logics - p. 321 42. 2.5: Summary - p. 335 43. 2.6: Exercises - p. 337 44. Chapter 3 Probabilistic Biomedical Knowledge - p. 343 45. 3.1: Probability and Statistics - p. 344 46. 3.2: Bayes’ Rule, Bayes Nets, and Probabilistic Inference - p. 364 47. 3.3: Utility and Decision Modeling - p. 375 48. 3.4: Information Theory - p. 382 49. 3.5: Summary - p. 395 50. 3.6: Exercises - p. 395 51. Chapter 4 Biomedical Information Access - p. 397 52. 4.1: Information Retrieval Systems - p. 398 53. 4.2: Beyond Text Documents - p. 413 54. 4.3: Networks and Biomedical Data Communication - p. 415 55. 4.4: Summary - p. 476 56. 4.5: Exercises - p. 476 57. Chapter 5 Computational Models and Methods - p. 479 58. 5.1: Computing with Genes, Proteins, and Cells - p. 480 59. 5.2: Search - p. 496 60. 5.3: Natural Language Processing - p. 523 61. 5.4: State Machines and Dynamic Models - p. 525 62. 5.5: Stochastic Processes - p. 562 63. 5.6: Summary - p. 575 64. 5.7: Exercises - p. 576 65. Chapter 6 Biomedical Data, Knowledge, and Systems in Context - p. 579 66. Chapter 7 Epilogue - p. 603 67. Appendix A Notes on Lisp, Other Languages, and Resources - p. 607 68. Bibliography - p. 627 69. Index - p. 647
