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|a 9781787286702
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|a (OCoLC)1048791966
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|a B06444
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|b .K863 2018
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|a HCDD
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| 100 |
1 |
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|a Kumar, Vikas (Vik)
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| 245 |
1 |
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|a Healthcare Analytics Made Simple :
|b Techniques in Healthcare Computing Using Machine Learning and Python.
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| 260 |
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|a Birmingham :
|b Packt Publishing Ltd,
|c 2018.
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| 300 |
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|a 1 online resource (258 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 588 |
0 |
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|a Print version record.
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0 |
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|a 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.
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| 505 |
8 |
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|a Using visualizations to elucidate patient carePredicting 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.
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| 505 |
8 |
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|a Advancing the adoption of electronic medical recordsPromoting 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).
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| 505 |
8 |
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|a 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.
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| 505 |
8 |
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|a Model frameworks for medical decision makingTree-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 -- ).
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| 500 |
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|a Calculating the post-test probability of MI given the presence of chest pain.
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| 520 |
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|a 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.
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| 650 |
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0 |
|a Medical care
|x Data processing.
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| 650 |
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0 |
|a Machine learning.
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| 650 |
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0 |
|a Python (Computer program language)
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| 650 |
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7 |
|a Machine learning
|2 fast
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| 650 |
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7 |
|a Medical care
|x Data processing
|2 fast
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| 650 |
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7 |
|a Python (Computer program language)
|2 fast
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| 758 |
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|i has work:
|a Healthcare analytics made simple (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFxHdFJQYrq6tRCk4qBy7d
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Kumar, Vikas (Vik).
|t Healthcare Analytics Made Simple : Techniques in Healthcare Computing Using Machine Learning and Python.
|d Birmingham : Packt Publishing Ltd, ©2018
|z 9781787286702
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5485013
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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