# healthcareai 2.5.0 Unreleased

#### Breaking changes

• healthcareai now depends on dplyr 1.0.0 and tibble 3.0.0. You will need these versions or later. Various hidden changes were made to be compatible with these packages’ lastest breaking changes.

#### Changed

• The allow_parallel parameter was removed to prevent nested parallelism.

# healthcareai 2.3.0 2018-12-12

#### Breaking changes

• healthcareai now depends on recipes 0.1.4 and caret 6.0.81. You will need these versions or later. Various hidden changes were made to be compatible with these packages’ lastest breaking changes.

#### Changed

• bagimpute in prep_data now accepts bag_trees to specify the number of trees. This is updated to be compatible with recipes 0.1.4.
• Local loaded healthcareai library versions now are saved to model objects.

# healthcareai 2.2.0 2018-09-01

• Support for models of outcomes with more than two classes (multiclass)
• Explore a model’s logic with explore. Make counterfactual predictions across the most-important features in a model to see how those features influence predicted outcomes.
• plot method to visualize a model’s logic.
• Identify opportunities to improve a patient’s outcome with Patient Impact Predictor, pip. Carefully specify variables and alternative values that exert causal influence on outcomes; then get recommended actions for a given patient with expected outcomes given the actions.
• Predict outcome groups based on how bad false alarms are relative to missed detections (outcome_groups argument to predict).
• Group predictions into risk groups using the risk_groups argument to predict.
• plot support for outcome- and risk-group predictions.
• Get thresholds to split outcome classes to optimize various performance metrics with get_thresholds.
• plot method to compare performance across metrics at various thresholds.
• split_train_test can keep multiple observations of an individual in the same split via the grouping_col argument.
• Replace values that represent missingness but have been interpreted by R as strings with NA with make_na.
• If missingness finds any such strings it issues a warning with code that can be used to do the replacement.
• Add counts to factor levels with rename_with_counts.
• summary.missingness method for wide datasets with missingness in many columns.

#### Changed

• In prep_data, trigonometric transformations make circular features out of dates and times for more informative features in less-wide data frames.
• Fixed AUPR in plot.model_class and summary.model_class.
• Can specify performance metric to optimize in machine_learn.
• missingness is faster.
• Predict on XGBoost models now works for any column order in the new dataset.
• Regression prediction plots are plotted at 1:1 aspect ratio.
• add_best_levels works in deployment even if none of the columns to be created are present in the deployment observations.
• prep_data can handle logical features.
• outcome doesn’t need to be re-declared in model training if it was specified in data prep.

#### Removed

• No longer support training models on un-prepped data.
• No longer support wrapping caret-trained models into a model_list.

# healthcareai 2.1.0 2018-07-01

• Identify values of high-cardinality variables that will make good features, even with multiple values per observation with add_best_levels and get_best_levels.
• glmnet for regularized linear and logistic regression.
• interpret and plot.interpret to extract glmnet estimates.
• XGBoost for regression and classification models.
• variable_importance returns random forest or xgboost importances, whichever model performs better.

#### Changed

• predict can now write an extensive log file, and if that option is activated, as in production, predict is a safe function that always completes; if there is an error, it returns a zero-row data frame that is otherwise the same as what would have been returned (provided prep_data or machine_learn was used).
• Control how low variance must be to remove columns by providing a numeric value to the remove_near_zero_variance argument of prep_data.
• Fixed bug in missingness that caused very small values to round to zero.
• Messages about time required for model training are improved.
• separate_drgs returns NA for complication when the DRG is missing.
• Removed some redundent training data from model_list objects.
• methods is attached on attaching the package so that scripts operate the same in Rscript, R GUI, and R Studio.
• Minor changes to maintain compatibility with ggplot2, broom, and recipes.

#### Removed

• Removed support for k-nearest neighbors
• Remove support for maxstat splitting rule in random forests

# healthcareai 2.0.0 2018-04-20

A whole new architecture featuring a simpler API, more rigor under the hood, and attractive plots.

# healthcareai 1.2.4 2018-02-14

• Patch to conform to CRAN policy of not writing to inst/
• Patch to maintain compatibility with ranger and caret
• Import methods to maintain functionality across environments
• Clean up of variation-exploration functions

# healthcareai 1.2.0 2017-10-27

• Limone – a lime-like model interpretation tool.
• Called via getProcessVariablesDf
• See examples at the end of the help files for RandomForestDeployment and LassoDeployment for usage details

# healthcareai 1.1.0 Unreleased

• Deploy now saves information about the model and deployment as an attribute of the output dataframe. This information is written to a log file in the working directory.
• skip_on_not_appveyor will skip a unit test unless it’s being run on Appveyor.

#### Changed

• Unit tests involving MSSQL now only run on Appveyor.

#### Removed

• skip_if_no_mssql isn’t needed as a test utility anymore.

# healthcareai 1.0.0 2017-09-11

• Multiclass functionality with XGBoost is supported using XGBoostDevelopment and XGBoostDeployment.
• K-means clustering is supported using KmeansClustering.
• findVariaion will return groups with the highest variation of a chosen target measure within a data set.
• variationAcrossGroups will plot a boxplot of variation between groups for a chosen target measure.

#### Changed

• SupervisedModelDevelopment now saves the model after training
• SupervisedModelDeployment no longer trains models. It only loads the model saved in SupervisedModelDevelopment. Predictions are made for all data.
• imputeColumn was replaced with imputeDF
• SQL tools now use a DBI backend. We support reading and writing to MSSQL and SQLite databases.
• SQL tools are now common functions used outside the algorithms.
• Model file documentation files now accurately reflect the available methods.

#### Removed

• testWindowCol is no longer a param in SupervisedModelDeployment or used in the algorithms.
• writeToDB is no longer a param in SupervisedModelDeployment or used in the algorithms.
• destSchemaTable is no longer a param in SupervisedModelDeployment or used in the algorithms.

# healthcareai 0.1.12 2017-05-05

• Added getters for predictions getPredictions() in development (lasso, random forest, linear mixed model)
• Added getOutDf to each algorithm deploy file so predictions can go to CSV
• Added percentDataAvailableInDateRange, to eventually replace countPercentEmpty

#### Changed

• TimeStamp column predictive output is now local time (not GMT)