healthcare analytics made simple o reilly

), Healthcare analytics improves medical care, Using visualizations to elucidate patient care, Predicting future diagnostic and treatment events, Measuring provider quality and performance, Protecting patient privacy and patient rights, Advancing the adoption of electronic medical records, Patient data – the journey from patient to computer, Additional objective data (lab tests, imaging, and other diagnostic tests), International Classification of Disease (ICD), Logical Observation Identifiers Names and Codes (LOINC), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), Putting it all together – specifying a use case, Model frameworks for medical decision making, 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, 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, Corresponding machine learning algorithms – linear and logistic regression, Corresponding machine learning algorithm – neural networks and deep learning, Receiver operating characteristic (ROC) curves, Data engineering with SQL – an example case, Case details – predicting mortality for a cardiology practice, 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, 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, Computing Foundations – Introduction to Python, Programming in Python – an illustrative example, Importing data into pandas from Python data structures, Importing data into pandas from a flat file, Importing data into pandas from a database, Adding new columns by transforming existing columns, 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, One-hot encoding of categorical variables, The Hospital Value-Based Purchasing (HVBP) program, The patient- and caregiver-centered experience of care domain, The Hospital Readmission Reduction (HRR) program, The Hospital-Acquired Conditions (HAC) program, The healthcare-acquired infections 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), The Healthcare Effectiveness Data and Information Set (HEDIS), Comparing dialysis facilities using Python, Importing the data into your Jupyter Notebook session, Displaying dialysis centers based on total performance, Introduction to predictive analytics in healthcare, Our modeling task – predicting discharge statuses for ED patients, Downloading the list of survey items – body_namcsopd.pdf, Downloading the documentation file – doc13_ed.pdf, Splitting the data into train and test sets, Healthcare Predictive Models – A Review, Predictive healthcare analytics – state of the art, Other applications of machine learning in CHF, An example – breast cancer prediction, Breast cancer screening and machine learning, The Future – Healthcare and Emerging Technologies, Predicting suicidality with machine learning, Obstacles, ethical issues, and limitations, Leave a review - let other readers know what you think, 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, Gain valuable insight into healthcare incentives, finances, and legislation, Discover the connection between machine learning and healthcare processes, 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, Get unlimited access to books, videos, and. 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