Healthcare analytics made simple : techniques in healthcare computing using machine learning and Python

1. Introduction to healthcare analytics -- What is healthcare analytics? -- Healthcare analytics uses advanced competing technology -- Healthcare analytics acts on the healthcare industry -- Healthcare analytics improves medical care -- Better outcomes -- Lower costs -- Ensure quality -- Foundation of healthcare analytics -- Healthcare -- Mathematics -- Computer science -- History of healthcare analytics -- Examples of healthcare analytic -- 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 -- 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 too computer -- The history and physical -- Metadata and chief complaint -- History of the present illness -- Past medial history -- Medications -- Family history -- Social history -- Allergies -- Review of systems -- Physical examination -- Additional objective data -- Assessment and plan -- The progress clinical note -- Standardized clinical code sets -- International classification of disease -- Current procedural terminology -- Logical observation identifiers names and codes -- National drug code -- Systematized nomenclature of medicine clinical terms -- 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 -- 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 -- 2x2 Contingency table for chest pain and myocardial infarction -- Interpreting the contingency table and calculating sensitivity and specificity -- Calculating likelihood ratios for chest pain -- 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 proprocessing the data -- aggregating data -- Parsing data -- Converting types -- Dealing with missing data -- Exploring and visualizing the data -- Selecting features -- Training the model parameters -- Evaluating model performance -- Sensitivity -- Specificity -- Positive predictive value -- Negative predictive value -- False-positive rate -- Accuracy -- receiver operating characteristics curves -- Precision-recall curves -- Continuously valued target variables -- 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 table s-- 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 - data 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 -- 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 -- 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 panda -- What is a pandas data frame? -- 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 data frames -- Adding columns -- Adding blank or user-initialized columns -- Adding new columns by transforming existing columns -- Dropping columns -- Applying functions to multiple columns -- Combining data frames -- Converting data frame columns to lists -- Getting and setting data frame 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 an iat -- Other operations -- Filtering rows using Boolean indexing -- Sorting rows -- SQL-like operations -- Getting aggregate row COUNTs -- Joining data frames -- 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 -- Performance assessment -- Additional analytics libraries -- NumPy and SciPy -- Matplotlib -- 6. Measuring healthcare quality -- Introduction to healthcare measures -- US medicare value-based programs -- The hospital value-based purchasing 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 program -- The hospital-acquired conditions program -- The healthcare-acquired infections domain -- The patient safety domain -- The end-stage renal disease quality incentive program -- The skilled nursing facility value-based program -- The home health value-based program -- The merit-based incentive payment system -- Quality -- Advancing care information -- Improvement activities -- Cost -- Other value-based programs -- The healthcare effectiveness data and information set -- State measures -- Comparing dialysis facilities using python -- Downloading the data -- Importing the data into your Jupyter notebook -- 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 -- 7. Making predictive models in healthcare -- Introduction to predictive analytics in healthcare -- Our modeling task - predicting discharge statuses for ED patients -- Obtaining the data set -- The NHAMCA data set at a glance -- Downloading the NHAMCS data -- Downloading the ED2013 -- file -- Downloading the list of survey items - body\_namcsoph.pdf -- Downloading the documentation file - doc13\_ed.pdf -- Starting a Jupyter session -- Importing the data set -- Loading the meta data -- Loading the ED data set -- Making the response variable -- Splitting the data into train and test sets -- Preprocesing the predictor variables -- Visit information -- Month -- Day of the week -- Arrival time -- Wait time -- Other visit information -- Demographic variables -- Age -- Sex -- ethnicity and race -- Other demographic information -- Triage variables-- Financial variables -- Vital signs -- Temperature -- Pulse -- Respiratory rate -- Blood pressure -- Oxygen saturation -- Pain level -- Reason-for-visit codes -- Injury codes -- Diagnostic codes -- Medical history -- Tests -- Procedures -- Medication codes -- Provider information -- disposition information -- Imputed columns -- Identifying variables -- electronic medical record status columns -- Detailed medication information -- Miscellaneous information -- Final preprocessing steps -- One-hot encoding -- Numeric conversion -- NomPy array conversion -- Building the models -- Logistic regression -- Random forests -- Neural network -- Using the models to make predictions -- Improving our models -- 8. Healthcare predictive models - a review -- Predictive healthcare analytics - state of the art -- Overall cardiovascular risk -- The framingham risk score -- Cardiovascular risk and machine learning -- Congestive heart failure -- Diagnosing CHF -- CHF detection with machine learning -- Other applications of machine learning in CHF -- Cancer -- What is cancer? -- ML applications for cancer -- Routine clinical data -- Cancer-specific clinical data -- Imaging data -- Genomic data -- Proteomic data -- An example - breast cancer prediction -- Traditional screening of breast cancer -- Breast cancer screening and machine learning -- Readmission prediction -- LACE and HOSPITAL scores -- Readmission modeling -- Other conditions and events -- 9. The future - healthcare and emerging technologies -- Healthcare analytics and the internet -- Healthcare analytics and the internet -- healthcare and the internet of things -- Healthcare analytics and social media -- Influenza surveillance and forecasting -- Predicting suicidality with machine learning -- Healthcare and deep learning -- What is deep learning, briefly? -- Deep learning in healthcare -- Deep feed-forward networks -- Convolutional neural networks for images -- Recurrent neural networks for sequences -- Obstacles, ethical issues, and limitations -- Obstacles -- Ethical issues -- Limitations;In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. Tap into the realm of social media and unleash the power of analytics for data-driven insights using RAbout This BookA practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media dataLearn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms.Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering.Who This Book Is ForIt is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise.What You Will LearnLearn how to tap into data from diverse social media platforms using the R ecosystemUse social media data to formulate and solve real-world problemsAnalyze user social networks and communities using concepts from graph theory and network analysisLearn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channelsUnderstand the art of representing actionable insights with effective visualizationsAnalyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so onLearn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many moreIn DetailThe Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.Style and approachThis book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on. Add a touch of data analytics to your healthcare systems and get insightful outcomes Key FeaturesPerform healthcare analytics with Python and SQLBuild predictive models on real healthcare data with pandas and scikit-learnUse analytics to improve healthcare performanceBook DescriptionIn recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learnGain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processesUse SQL and Python to analyze dataMeasure healthcare quality and provider performanceIdentify features and attributes to build successful healthcare models Build predictive models using real-world healthcare dataBecome an expert in predictive modeling with structured clinical dataSee what lies ahead for healthcare analyticsWho this book is forHealthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare. Table of ContentsIntroduction to Healthcare AnalyticsHealthcare FoundationsMachine Learning FoundationsComputing Foundations - DatabasesComputing Foundations - Introduction to PythonMeasuring Healthcare QualityMaking Predictive Models in HealthcareHealthcare Predictive Models - A Review The Future - Healthcare and Emerging Technologies Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow. About This Book A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book Understand deep learning and predictive analytics along with its challenges and best practices Who This Book Is For This book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need! What You Will Learn Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling Develop predictive models using classification, regression, and clustering algorithms Develop predictive models for NLP Learn how to use reinforcement learning for predictive analytics Factorization Machines for advanced recommendation systems Get a hands-on understanding of deep learning architectures for advanced predictive analytics Learn how to use deep Neural Networks for predictive analytics See how to use recurrent Neural Networks for predictive analytics Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysis In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence. This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling. The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system. The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis. Style and app .. Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare. Downloading the example code for this book You can download the example code files for all Pa .. Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical r.. BAdd a touch of data analytics to your healthcare systems and get insightful outcomes/b h4Key Features/h4 ulliPerform healthcare analytics with Python and SQL /li liBuild predictive models on real healthcare data with pandas and scikit-learn /li liUse analytics to improve healthcare performance/li/ul h4Book Description/h4 In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. h4What you will learn/h4 ulliGain valuable insight into healthcare incentives, finances, and legislation /li liDiscover the connection between machine learning and healthcare processes /li liUse SQL and Python to analyze data /li liMeasure healthcare quality and provider performance /li liIdentify features and attributes to build successful healthcare models /li liBuild predictive models using real-world healthcare data /li liBecome an expert in predictive modeling with structured clinical data /li liSee what lies ahead for healthcare analytics/li/ul h4Who this book is for/h4 Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare About This BookA quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and textsBuild your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the bookUnderstand deep learning and predictive analytics along with its challenges and best practicesWho This Book Is ForThis book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need!What You Will LearnSolid & theoretical understanding of linear algebra, statistics & probability for predictive modelingDevelop predictive models using classification, regression & clustering algorithmsDevelop predictive models for NLPReinforcement learning for predictive analyticsFactorization Machines for advanced recommendation systemsHands-on understanding of deep learning architectures for advanced predictive analyticsDeep Neural Networks for predictive analyticsRecurrent Neural Networks for predictive analyticsConvolutional Neural Networks for emotion recognition, image classification & sentiment analysis.In DetailPredictive analytics allows discovering hidden patterns from structured & unstructured data for automated decision making in business intelligence.This book will help you build, tune & deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics & probability theory for predictive modeling.The second section shows developing predictive models via supervised (classification, regression) & unsupervised (clustering) algorithms. It then exhibits developing predictive models for NLP and covers reinforcement learning algorithms. Lastly, developing a Factorization Machines-based recommendation system is shown.The third section covers deep learning architectures for advanced predictive analytics: including, Deep Neural Networks & Recurrent Neural Networks for high-dimensional and sequence data. Finally, Convolutional Neural Networks is used for predictive modeling for emotion recognition, image classification & sentiment analysis. Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform... COM021030 - COMPUTERS / Databases / Data Mining,COM018000 - COMPUTERS / Data Processing,COM021030 - COMPUTERS / Databases / Data Mining