Healthcare Analytics Made Simple : Techniques in Healthcare Computing Using Machine Learning and Python.

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.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kumar, Vikas (Vik)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing Ltd, 2018.
Temas:
Medical care > Data processing.
Machine learning.
Python (Computer program language)
Machine Learning
Apprentissage automatique.
Python (Langage de programmation)
Soins médicaux > Informatique.
Machine learning
Medical care > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 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.
  • 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).
  • 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 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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