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E-raamat: Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby

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Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and RubySuurem pilt
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Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clinical and research datasets. The common computational tasks of medical informatics are accessible to anyone willing to learn the basics.

Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby demonstrates that biomedical professionals with fundamental programming knowledge can master any kind of data collection. Providing you with access to data, nomenclatures, and programming scripts and languages that are all free and publicly available, this book











Describes the structure of data sources used, with instructions for downloading Includes a clearly written explanation of each algorithm Offers equivalent scripts in Perl, Python, and Ruby, for each algorithm Shows how to write short, quickly learned scripts, using a minimal selection of commands Teaches basic informatics methods for retrieving, organizing, merging, and analyzing data sources Provides case studies that detail the kinds of questions that biomedical scientists can ask and answer with public data and an open source programming language

Requiring no more than a working knowledge of Perl, Python, or Ruby, Methods in Medical Informatics will have you writing powerful programs in just a few minutes. Within its chapters, you will find descriptions of the basic methods and implementations needed to complete many of the projects you will encounter in your biomedical career.

Arvustused

As subspecialty board certification in clinical informatics has finally become a reality, Jules Bermans Methods in Medical Informatics could not be more timely. This well-written and informative text combines Dr. Bermans expertise in programming with his vast knowledge of publicly available data sets and everyday healthcare programming needs to result in a book which should become a staple in health informatics education programs as well as a standard addition to the personal libraries of informaticists. Alexis B. Carter, Journal of Pathology Informatics, October 2011

This book provides an introduction to processing clinical and population health data using rigorous methods and widely available, low cost, but very capable tools. The inclusion of the three leading dynamic programming languages broadens the appeal bridges the gap from programming instruction to dealing with specialized medical data, making it possible to teach a relevant programming course in a biomedical environment. I would have loved to have a copy of this when I was teaching introductory programming for medical informatics. Professor James H. Harrison, Jr., Director of Clinical Informatics, University of Virginia

presents students and professionals in the healthcare field (who have some working knowledge of the open-source programming languages Perl, Python, or Ruby) with instruction for applying basic informatics algorithms to medical data sets. He [ the author] provides algorithm scripts for each of the languages, along with step-by-step explanations of the algorithms used for retrieving, organizing, merging, and analyzing such data sources as the National Cancer Institutes Surveillance Epidemiology and End Results project, the National Library of Medicines PubMed service, the mortality records of the US Centers for Disease Control and Prevention, the US Census, and the Online Mendelian Inheritance in Man data set on inherited conditions. SciTech Book News, February 2011

Preface xv
Nota Bene xxi
About the Author xxiii
Part I Fundamental Algorithms and Methods of Medical Informatics
Chapter 1 Parsing and Transforming Text Files
3(18)
1.1 Peeking into Large Files
3(2)
1.1.1 Script Algorithm
3(2)
1.1.2 Analysis
5(1)
1.2 Paging through Large Text Files
5(2)
1.2.1 Script Algorithm
5(2)
1.2.2 Analysis
7(1)
1.3 Extracting Lines that Match a Regular Expression
7(3)
1.3.1 Script Algorithm
8(2)
1.3.2 Analysis
10(1)
1.4 Changing Every File in a Subdirectory
10(2)
1.4.1 Script Algorithm
10(1)
1.4.2 Analysis
11(1)
1.5 Counting the Words in a File
12(2)
1.5.1 Script Algorithm
12(2)
1.5.2 Analysis
14(1)
1.6 Making a Word List with Occurrence Tally
14(2)
1.6.1 Script Algorithm
14(2)
1.6.2 Analysis
16(1)
1.7 Using Printf Formatting Style
16(5)
1.7.1 Script Algorithm
17(1)
1.7.2 Analysis
18(3)
Chapter 2 Utility Scripts
21(16)
2.1 Random Numbers
21(1)
2.1.1 Script Algorithm
21(1)
2.1.2 Analysis
22(1)
2.2 Converting Non-ASCII to Base64 ASCII
22(2)
2.2.1 Script Algorithm
23(1)
2.2.2 Analysis
24(1)
2.3 Creating a Universally Unique Identifier
24(1)
2.3.1 Script Algorithm
24(1)
2.3.2 Analysis
25(1)
2.4 Splitting Text into Sentences
25(2)
2.4.1 Script Algorithm
26(1)
2.4.2 Analysis
26(1)
2.5 One-Way Hash on a Name
27(3)
2.5.1 Script Algorithm
28(2)
2.5.2 Analysis
30(1)
2.6 One-Way Hash on a File
30(1)
2.6.1 Script Algorithm
30(1)
2.6.2 Analysis
31(1)
2.7 A Prime Number Generator
31(6)
2.7.1 Script Algorithm
32(2)
2.7.2 Analysis
34(3)
Chapter 3 Viewing and Modifying Images
37(16)
3.1 Viewing a JPEG Image
37(3)
3.1.1 Script Algorithm
38(1)
3.1.2 Analysis
39(1)
3.2 Converting between Image Formats
40(2)
3.2.1 Script Algorithm
40(1)
3.2.2 Analysis
41(1)
3.3 Batch Conversions
42(2)
3.3.1 Script Algorithm
42(1)
3.3.2 Analysis
43(1)
3.4 Drawing a Graph from List Data
44(2)
3.4.1 Script Algorithm
44(2)
3.4.2 Analysis
46(1)
3.5 Drawing an Image Mashup
46(7)
3.5.1 Script Algorithm
46(4)
3.5.2 Analysis
50(3)
Chapter 4 Indexing Text
53(28)
4.1 ZIPF Distribution of Text File
53(4)
4.1.1 Script Algorithm
54(2)
4.1.2 Analysis
56(1)
4.2 Preparing a Concordance
57(3)
4.2.1 Script Algorithm
57(2)
4.2.2 Analysis
59(1)
4.3 Extracting Phrases
60(3)
4.3.1 Script Algorithm
61(2)
4.3.2 Analysis
63(1)
4.4 Preparing an Index
63(6)
4.4.1 Script Algorithm
65(3)
4.4.2 Analysis
68(1)
4.5 Comparing Texts Using Similarity Scores
69(12)
4.5.1 Script Algorithm
69(7)
4.5.2 Analysis
76(5)
Part II Medical Data Resources
Chapter 5 The National Library of Medicine's Medical Subject Headings (MESH)
81(18)
5.1 Determining the Hierarchical Lineage for MeSH Terms
83(5)
5.1.1 Sceript Algorithm
83(3)
5.1.2 Analysis
86(2)
5.2 Creating a MeSH Database
88(2)
5.2.1 Script Algorithm
88(2)
5.2.2 Analysis
90(1)
5.3 Reading the MeSH Database
90(2)
5.3.1 Script Algorithm
91(1)
5.3.2 Analysis
92(1)
5.4 Creating an SQLite Database for MeSH
92(4)
5.4.1 Script Algorithm
93(3)
5.4.2 Analysis
96(1)
5.5 Reading the SQLite MeSH Database
96(3)
5.5.1 Script Algorithm
96(1)
5.5.2 Analysis
97(2)
Chapter 6 The International Classification of Diseases
99(8)
6.1 Creating the ICD Dictionary
99(3)
6.1.1 Script Algorithm
100(1)
6.1.2 Analysis
101(1)
6.2 Building the ICD-O (Oncology) Dictionary
102(5)
6.2.1 Script Algorithm
103(1)
6.2.2 Analysis
104(3)
Chapter 7 Seer: The Cancer Surveillance, Epidemiology, and End Results Program
107(16)
7.1 Parsing the SEER Data Files
107(3)
7.1.1 Script Algorithm
107(2)
7.1.2 Analysis
109(1)
7.2 Finding the Occurrences of All Cancers in the SEER Data Files
110(5)
7.2.1 Script Algorithm
111(3)
7.2.2 Analysis
114(1)
7.3 Finding the Age Distributions of the Cancers in the SEER Data Files
115(8)
7.3.1 Script Algorithm
115(4)
7.3.2 Analysis
119(4)
Chapter 8 OMIM: The Online Mendelian Inheritance in Man
123(8)
8.1 Collecting the OMIM Entry Terms
124(2)
8.1.1 Script Algorithm
124(1)
8.1.2 Analysis
125(1)
8.2 Finding Inherited Cancer Conditions
126(5)
8.2.1 Script Algorithm
126(2)
8.2.2 Analysis
128(3)
Chapter 9 PubMed
131(12)
9.1 Building a Large Text Corpus of Biomedical Information
131(3)
9.1.1 Script Algorithm
132(2)
9.1.2 Analysis
134(1)
9.2 Creating a List of Doublets from a PubMed Corpus
134(5)
9.2.1 Script Algorithm
136(2)
9.2.2 Analysis
138(1)
9.3 Downloading Gene Synonyms from PubMed
139(1)
9.4 Downloading Protein Synonyms from PubMed
140(3)
Chapter 10 Taxonomy
143(14)
10.1 Finding a Taxonomic Hierarchy
143(5)
10.1.1 Script Algorithm
144(3)
10.1.2 Analysis
147(1)
10.2 Finding the Restricted Classes of Human Infectious Pathogens
148(9)
10.2.1 Script Algorithm
148(5)
10.2.2 Analysis
153(4)
Chapter 11 Developmental Lineage Classification and Taxonomy of Neoplasms
157(20)
11.1 Building the Doublet Hash
158(3)
11.1.1 Script Algorithm
158(3)
11.1.2 Analysis
161(1)
11.2 Scanning the Literature for Candidate Terms
161(6)
11.2.1 Script Algorithm
161(5)
11.2.2 Analysis
166(1)
11.3 Adding Terms to the Neoplasm Classification
167(4)
11.3.1 Script Algorithm
168(2)
11.3.2 Analysis
170(1)
11.4 Determining the Lineage of Every Neoplasm Concept
171(6)
11.4.1 Script Algorithm
172(3)
11.4.2 Analysis
175(2)
Chapter 12 U.S. Census Files
177(16)
12.1 Total Population of the United States
177(5)
12.1.1 Script Algorithm
177(4)
12.1.2 Analysis
181(1)
12.2 Stratified Distribution for the U.S. Census
182(3)
12.2.1 Script Algorithm
182(2)
12.2.2 Analysis
184(1)
12.3 Adjusting for Age
185(8)
12.3.1 Script Algorithm
186(3)
12.3.2 Analysis
189(4)
Chapter 13 Centers for Disease Control and Prevention Mortality Files
193(16)
13.1 Death Certificate Data
193(3)
13.2 Obtaining the CDC Data Files
196(1)
13.3 How Death Certificates Are Represented in Data Records
197(3)
13.4 Ranking, by Number of Occurrences, Every Condition in the CDC Mortality Files
200(9)
13.4.1 Script Algorithm
200(4)
13.4.2 Analysis
204(5)
Part III Primary Tasks of Medical Informatics
Chapter 14 Autocoding
209(10)
14.1 A Neoplasm Autocoder
209(7)
14.1.1 Script Algorithm
210(5)
14.1.2 Analysis
215(1)
14.2 Recoding
216(3)
Chapter 15 Text Scrubber for Deidentifying Confidential Text
219(8)
15.1 Script Algorithm
220(2)
15.2 Analysis
222(5)
Chapter 16 Web Pages and CGI Scripts
227(10)
16.1 Grabbing Web Pages
227(3)
16.1.1 Script Algorithm
227(2)
16.1.2 Analysis
229(1)
16.2 CGI Script for Searching the Neoplasm Classification
230(7)
16.2.1 Script Algorithm
231(4)
16.2.2 Analysis
235(2)
Chapter 17 Image Annotation
237(12)
17.1 Inserting a Header Comment
238(2)
17.1.1 Script Algorithm
238(2)
17.1.2 Analysis
240(1)
17.2 Extracting the Header Comment in a JPEG Image File
240(2)
17.2.1 Script Algorithm
240(1)
17.2.2 Analysis
241(1)
17.3 Inserting IPTC Annotations
242(1)
17.4 Extracting Comment, EXIF, and IPTC Annotations
242(1)
17.4.1 Script Algorithm
242(1)
17.4.2 Analysis
242(1)
17.5 Dealing with DICOM
243(1)
17.6 Finding DICOM Images
244(1)
17.7 DICOM-to-JPEG Conversion
245(4)
17.7.1 Script Algorithm
245(1)
17.7.2 Analysis
246(3)
Chapter 18 Describing Data with Data, Using XML
249(20)
18.1 Parsing XML
250(4)
18.1.1 Script Algorithm
250(2)
18.1.2 Analysis
252(1)
18.1.3 Resource Description Framework (RDF)
252(2)
18.2 Dublin Core Metadata
254(1)
18.3 Insert an RDF Document into an Image File
254(2)
18.3.1 Script Algorithm
255(1)
18.3.2 Analysis
256(1)
18.4 Insert an Image File into an RDF Document
256(3)
18.4.1 Script Algorithm
257(1)
18.4.2 Analysis
258(1)
18.5 RDF Schema
259(1)
18.6 Visualizing an RDF Schema with GraphViz
260(2)
18.7 Obtaining GraphViz
262(1)
18.8 Converting a Data Structure to GraphViz
263(6)
18.8.1 Script Algorithm
263(2)
18.8.2 Analysis
265(4)
Part IV Medical Discovery
Chapter 19 Case Study: Emphysema Rates
269(6)
19.1 Script Algorithm
270(3)
19.2 Analysis
273(2)
Chapter 20 Case Study: Cancer Occurrence Rates
275(10)
20.1 Script Algorithm
275(6)
20.2 Analysis
281(4)
Chapter 21 Case Study: Germ Cell Tumor Rates Across Ethnicities
285(10)
21.1 Script Algorithm
286(7)
21.2 Analysis
293(2)
Chapter 22 Case Study: Ranking the Death-Certifying Process, By State
295(6)
22.1 Script Algorithm
295(3)
22.2 Analysis
298(3)
Chapter 23 Case Study: Data Mashups for Epidemics
301(14)
23.1 Tally of Coccidioidomycosis Cases by State
302(5)
23.1.1 Script Algorithm
303(3)
23.1.2 Analysis
306(1)
23.2 Creating the Map Mashup
307(8)
23.2.1 Script Algorithm
307(4)
23.2.2 Analysis
311(4)
Chapter 24 Case Study: Sickle Cell Rates
315(6)
24.1 Script Algorithm
315(3)
24.2 Analysis
318(3)
Chapter 25 Case Study: Site-Specific Tumor Biology
321(14)
25.1 Anatomic Origins of Mesotheliomas
321(2)
25.2 Mesothelioma Records in the SEER Data Sets
323(6)
25.2.1 Script Algorithm
324(5)
25.2.2 Analysis
329(1)
25.3 Graphic Representation
329(6)
25.3.1 Script Algorithm
330(3)
25.3.2 Analysis
333(2)
Chapter 26 Case Study: Bimodal Tumors
335(16)
26.1 Script Algorithm
337(7)
26.2 Analysis
344(7)
Chapter 27 Case Study: The Age of Occurrence of Precancers
351(10)
27.1 Script Algorithm
351(6)
27.2 Analysis
357(4)
Epilogue for Healthcare Professionals and Medical Scientists
361(6)
Learn One or More Open Source Programming Languages
361(1)
Don't Agonize Over Which Language You Should Choose
362(1)
Learn Algorithms
362(1)
Unless You Are a Professional Programmer, Relax and Enjoy Being a Newbie
363(1)
Do Not Delegate Simple Programming Tasks to Others
363(1)
Break Complex Tasks into Simple Methods and Algorithms
364(1)
Write Fast Scripts
364(1)
Concentrate on the Questions, Not the Answers
365(2)
Appendix
367(10)
How to Acquire Ruby
367(1)
How to Acquire Perl
367(1)
How to Acquire Python
367(1)
How to Acquire RMagick
368(1)
How to Acquire SQLite
369(1)
How to Acquire the Public Data Files Used in This Book
370(6)
Other Publicly Available Files, Data Sets, and Utilities
376(1)
Index 377
Jules Berman, Ph.D., M.D., received two bachelor of science degrees (mathematics and earth sciences) from MIT, a Ph.D. in pathology from Temple University, and an M.D. from the University of Miami School of Medicine. His postdoctoral research was conducted at the National Cancer Institute. His medical residence in pathology was completed at the George Washington University School of Medicine. He became board certified in anatomic pathology and in cytopathology, and served as the chief of Anatomic Pathology, Surgical Pathology and Cytopathology at the Veterans Administration (VA) Medical Center in Baltimore, Maryland.

While at the Baltimore VA, Dr. Berman held appointments at the University of Maryland Medical Center and at theJohns Hopkins Medical Institutions. In 1998, he became the program director for pathology informatics in the Cancer Diagnosis Program at the U.S. National Cancer Institute. In 2006, he became president of the Association for Pathology Informatics. Over the course of his career, he has written, as first author, more than 100 publications, including five books in the field of medical informatics. Today, Dr. Berman is a full-time freelance writer.
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