Healthcare Analytics Made Simple : (Record no. 4160)
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| fixed length control field | 11454nam a22005053i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC5485013 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724113312.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2018 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781787283220 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781787286702 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC5485013 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL5485013 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1048791966 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | R858 .K863 2018 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610.285 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kumar, Vikas (Vik). |
| 245 10 - TITLE STATEMENT | |
| Title | Healthcare Analytics Made Simple : |
| Remainder of title | Techniques in Healthcare Computing Using Machine Learning and Python. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Birmingham : |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing, Limited, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2018. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (258 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to Healthcare Analytics -- What is healthcare analytics? -- Healthcare analytics uses advanced computing technology -- Healthcare analytics acts on the healthcare industry (DUH!) -- Healthcare analytics improves medical care -- Better outcomes -- Lower costs -- Ensure quality -- Foundations of healthcare analytics -- Healthcare -- Mathematics -- Computer science -- History of healthcare analytics -- Examples of healthcare analytics -- Using visualizations to elucidate patient care -- Predicting future diagnostic and treatment events -- Measuring provider quality and performance -- Patient-facing treatments for disease -- Exploring the software -- Anaconda -- Anaconda navigator -- Jupyter notebook -- Spyder IDE -- SQLite -- Command-line tools -- Installing a text editor -- Summary -- References -- Chapter 2: Healthcare Foundations -- Healthcare delivery in the US -- Healthcare industry basics -- Healthcare financing -- Fee-for-service reimbursement -- Value-based care -- Healthcare policy -- Protecting patient privacy and patient rights -- Advancing the adoption of electronic medical records -- Promoting value-based care -- Advancing analytics in healthcare -- Patient data - the journey from patient to computer -- The history and physical (H& -- P) -- Metadata and chief complaint -- History of the present illness (HPI) -- Past medical history -- Medications -- Family history -- Social history -- Allergies -- Review of systems -- Physical examination -- Additional objective data (lab tests, imaging, and other diagnostic tests) -- Assessment and plan -- The progress (SOAP) clinical note -- Standardized clinical codesets -- International Classification of Disease (ICD). |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Current Procedural Terminology (CPT) -- Logical Observation Identifiers Names and Codes (LOINC) -- National Drug Code (NDC) -- Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) -- Breaking down healthcare analytics -- Population -- Medical task -- Screening -- Diagnosis -- Outcome/Prognosis -- Response to treatment -- Data format -- Structured -- Unstructured -- Imaging -- Other data format -- Disease -- Acute versus chronic diseases -- Cancer -- Other diseases -- Putting it all together - specifying a use case -- Summary -- References and further reading -- Chapter 3: Machine Learning Foundations -- Model frameworks for medical decision making -- Tree-like reasoning -- Categorical reasoning with algorithms and trees -- Corresponding machine learning algorithms - decision tree and random forest -- Probabilistic reasoning and Bayes theorem -- Using Bayes theorem for calculating clinical probabilities -- Calculating the baseline MI probability -- 2 x 2 contingency table for chest pain and myocardial infarction -- Interpreting the contingency table and calculating sensitivity and specificity -- Calculating likelihood ratios for chest pain (+ and -) -- Calculating the post-test probability of MI given the presence of chest pain -- Corresponding machine learning algorithm - the Naive Bayes Classifier -- Criterion tables and the weighted sum approach -- Criterion tables -- Corresponding machine learning algorithms - linear and logistic regression -- Pattern association and neural networks -- Complex clinical reasoning -- Corresponding machine learning algorithm - neural networks and deep learning -- Machine learning pipeline -- Loading the data -- Cleaning and preprocessing the data -- Aggregating data -- Parsing data -- Converting types -- Dealing with missing data -- Exploring and visualizing the data -- Selecting features. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Training the model parameters -- Evaluating model performance -- Sensitivity (Sn) -- Specificity (Sp) -- Positive predictive value (PPV) -- Negative predictive value (NPV) -- False-positive rate (FPR) -- Accuracy (Acc) -- Receiver operating characteristic (ROC) curves -- Precision-recall curves -- Continuously valued target variables -- Summary -- References and further reading -- Chapter 4: Computing Foundations - Databases -- Introduction to databases -- Data engineering with SQL - an example case -- Case details - predicting mortality for a cardiology practice -- The clinical database -- The PATIENT table -- The VISIT table -- The MEDICATIONS table -- The LABS table -- The VITALS table -- The MORT table -- Starting an SQLite session -- Data engineering, one table at a time with SQL -- Query Set #0 - creating the six tables -- Query Set #0a - creating the PATIENT table -- Query Set #0b - creating the VISIT table -- Query Set #0c - creating the MEDICATIONS table -- Query Set #0d - creating the LABS table -- Query Set #0e - creating the VITALS table -- Query Set #0f - creating the MORT table -- Query Set #0g - displaying our tables -- Query Set #1 - creating the MORT_FINAL table -- Query Set #2 - adding columns to MORT_FINAL -- Query Set #2a - adding columns using ALTER TABLE -- Query Set #2b - adding columns using JOIN -- Query Set #3 - date manipulation - calculating age -- Query Set #4 - binning and aggregating diagnoses -- Query Set #4a - binning diagnoses for CHF -- Query Set #4b - binning diagnoses for other diseases -- Query Set #4c - aggregating cardiac diagnoses using SUM -- Query Set #4d - aggregating cardiac diagnoses using COUNT -- Query Set #5 - counting medications -- Query Set #6 - binning abnormal lab results -- Query Set #7 - imputing missing variables. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Query Set #7a - imputing missing temperature values using normal-range imputation -- Query Set #7b - imputing missing temperature values using mean imputation -- Query Set #7c - imputing missing BNP values using a uniform distribution -- Query Set #8 - adding the target variable -- Query Set #9 - visualizing the MORT_FINAL_2 table -- Summary -- References and further reading -- Chapter 5: Computing Foundations - Introduction to Python -- Variables and types -- Strings -- Numeric types -- Data structures and containers -- Lists -- Tuples -- Dictionaries -- Sets -- Programming in Python - an illustrative example -- Introduction to pandas -- What is a pandas DataFrame? -- Importing data -- Importing data into pandas from Python data structures -- Importing data into pandas from a flat file -- Importing data into pandas from a database -- Common operations on DataFrames -- Adding columns -- Adding blank or user-initialized columns -- Adding new columns by transforming existing columns -- Dropping columns -- Applying functions to multiple columns -- Combining DataFrames -- Converting DataFrame columns to lists -- Getting and setting DataFrame values -- Getting/setting values using label-based indexing with loc -- Getting/setting values using integer-based labeling with iloc -- Getting/setting multiple contiguous values using slicing -- Fast getting/setting of scalar values using at and iat -- Other operations -- Filtering rows using Boolean indexing -- Sorting rows -- SQL-like operations -- Getting aggregate row COUNTs -- Joining DataFrames -- Introduction to scikit-learn -- Sample data -- Data preprocessing -- One-hot encoding of categorical variables -- Scaling and centering -- Binarization -- Imputation -- Feature-selection -- Machine learning algorithms -- Generalized linear models -- Ensemble methods -- Additional machine learning algorithms. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Performance assessment -- Additional analytics libraries -- NumPy and SciPy -- matplotlib -- Summary -- Chapter 6: Measuring Healthcare Quality -- Introduction to healthcare measures -- US Medicare value-based programs -- The Hospital Value-Based Purchasing (HVBP) program -- Domains and measures -- The clinical care domain -- The patient- and caregiver-centered experience of care domain -- Safety domain -- Efficiency and cost reduction domain -- The Hospital Readmission Reduction (HRR) program -- The Hospital-Acquired Conditions (HAC) program -- The healthcare-acquired infections domain -- The patient safety domain -- The End-Stage Renal Disease (ESRD) quality incentive program -- The Skilled Nursing Facility Value-Based Program (SNFVBP) -- The Home Health Value-Based Program (HHVBP) -- The Merit-Based Incentive Payment System (MIPS) -- Quality -- Advancing care information -- Improvement activities -- Cost -- Other value-based programs -- The Healthcare Effectiveness Data and Information Set (HEDIS) -- State measures -- Comparing dialysis facilities using Python -- Downloading the data -- Importing the data into your Jupyter Notebook session -- Exploring the data rows and columns -- Exploring the data geographically -- Displaying dialysis centers based on total performance -- Alternative analyses of dialysis centers -- Comparing hospitals -- Downloading the data -- Importing the data into your Jupyter Notebook session -- Exploring the tables -- Merging the HVBP tables -- Summary -- References -- Chapter 7: Making Predictive Models in Healthcare -- Introduction to predictive analytics in healthcare -- Our modeling task - predicting discharge statuses for ED patients -- Obtaining the dataset -- The NHAMCS dataset at a glance -- Downloading the NHAMCS data -- Downloading the ED2013 file -- Downloading the list of survey items - body_namcsopd.pdf. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Downloading the documentation file - doc13_ed.pdf. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Machine learning and analytics have been widely utilized across the healthcare sector of late. This book will bridge the gap between practicing doctors and you as a data scientist. You will learn how to work with healthcare data and gain better insight from this data to improve healthcare outcomes. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Medical care-Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Python (Computer program language). |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Kumar, Vikas (Vik) |
| Title | Healthcare Analytics Made Simple |
| Place, publisher, and date of publication | Birmingham : Packt Publishing, Limited,c2018 |
| International Standard Book Number | 9781787286702 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5485013">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5485013</a> |
| Public note | Click to View |
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