PatientLevelPrediction 6.6.0

New features

• Added flexible hyperparameter tuning with configurable tuning metrics and support for grid, random, and custom search strategies (#618).
• Standardized the non-Cyclops modeling interface to simplify tuning and maintenance of classification models (#618).
• Added ridge logistic regression settings via Cyclops with setRidgeRegression() (#621).
• Expanded imputation support and hardened the missing-indicator and predictive mean matching workflow (#622).
• Added support for using logits / linear predictors in rank-based metrics (#615).
• Persisted hyperparameter settings and model names in the results data model to improve downstream model identification and viewing (#633, #632).

Bug fixes

• Improved upload of hyperparameter metadata and robustness of model settings persistence for database viewers and downstream tools (#628, #623).
• Ensured existing GLM and scikit-learn model settings retain model identity so uploads generate distinct model design records (#614).
• Fixed evaluation when outcomes are single-class (#624).
• Improved LightGBM model persistence using a more robust in-memory serialization path (#626).
• Removed deprecated sklearn AdaBoost usage for compatibility with newer scikit-learn versions (#627).
• Fixed serialization of simpleImputer metadata when saving PLP models (#630).
• Limited batchRestrict handling to SQLite-backed data to avoid incorrect behavior on other backends (#612).

Performance and maintenance

• Improved simpleImpute performance for large feature sets (#629).
• Reduced GitHub Actions R CMD check runtime in CI (#625).

PatientLevelPrediction 6.5.1

• [Feature] Added optional missingness indicators for features imputed with createSimpleImputer() and createIterativeImputer() via addMissingIndicator (default FALSE for backward compatibility)
• [Bug] Remove old R condition from Rd files throug new roxygen2 (PR #603)
• [Internal] Use reticulate’s py_require for stating python dependencies (PR #600)
• [Bug] use closeConnection instead of finalize for RMM (PR #599)
• [Bug] Refresh the xgboost training call to remove watchlist/nthread deprecation warnings (PR #587)
• [Bug] Align querySql() usage and migration discovery with DatabaseConnector 7 to keep PLP uploads and exports working (PR #595)
• [Internal] Add pkgload to Suggests for dev/test tooling (PR #595)
• [Bug] Improve net benefit plot to handle multiple results and thresholdSummary (PR #586)

PatientLevelPrediction 6.5.0

• [Feature] skip diagnostics when using runMultiplePlp (PR #581)
• [Bug] Add seed to simulatePlpData to stop convergence issues in examples (PR #583)
• [Bug] Conserve andromeda object when samplig (PR #579)
• [Bug] Fix modelSettings of existing GLM (PR #575)
• [Bug] Fix normalizers if feature is a constant (PR #573)
• [Bug] Fix failure of rclassifier and sklearn models with only one feature (PR #569)

PatientLevelPrediction 6.4.1

• [Bug] Fix bug when sampleSize is NULL but restricting by dates in getPlpData (PR #559)
• [Bug] Add cohort definitions to external validation (PR #562)
• [Bug] Copy attributes correctly in new FE methods (PR #557)
• [Bug] Specify more settings for existing models (GLM and Scikit-learn) (PR #555)
• [Bug] temporal data compatibility (PR #554)
• [Bug] Specify start and endDays in cohortCovariates (PR #553)
• [Bug] Fix bugs when uploading same cohorts in parallel (PR #544)

PatientLevelPrediction 6.4.0

• Official maintainer updated to Egill Fridgeirsson
• Feature: Added experimental imputation methods
  • Simple imputation using median or mean
  • Iterative imputation by chained equations using predictive mean matchin
• Feature: Added more feature engineering methods to deal with rare features and normalization of numerical features
  • Min/Max normalization
  • Robust normalization which normalizes by the interquartile range and optionally squashes the features to a range -3 to 3
• Feature: Added existingSplit settings where users can split data bases on an existing split
• Feature: Added a net benefit plot plotNetBenefit
• Feature: Added timings that are printed to the log to most functions in the pipeline
• Feature: Now a model trained with scikit-learn in python can be converted with createSklearnModel and used to predict on new data with the package
• Feature: Added a convenience function getEunomiaPlpData to get some data in one line.
• Docs: Added a new GIS example to the docs (thanks @jshoughtaling)
• Docs: All exported functions (about 120) now have runnable examples. All examples are now conditinally executed if required. Shiny examples only executed in interactive sessions, examples using packages in suggests only executed if they are installed and so on.
• Docs: Fixed linting errors and R codestyle in docs to conform to HADES style
• Docs: Remove links to pdf’s, point to website instead.
• Docs: Fix broken links in Readme and BuildingPredictiveModels vignette
• Internal: Added support for coming duckdb backed Andromeda
  • Only create indexes for sqlite backed Andromeda
• Internal: Removed the old simulationProfile and added a new one based on Eunomia
  • This will allow us to quickly simulate small datasets for examples
  • Manually specified an outcome model tailored to Eunomia
• Internal: Went through all tests and put skip_if_offline or skip_if_not_installed where appropriate. Tests using Eunomia need internet and tests using any suggest packages need skip_if_not_installed. Put skip_on_cran for all tests requiring python
• Internal: Cleaned up dependencies. Moved what could be moved to suggest and put behind a rlang::check_installed flag. This means the installation is much lighter if only using the basic functionality of the package, e.g. develop a model using Cyclops.
• CI: Use UV for python management on github actions and upgrade ubuntu version to 24.04
• CI: Added an action to detect broken links in repo

PatientLevelPrediction 6.3.9

• Hotfix adding schema to DatabaseConnector::getTableNames when creating results tables

PatientLevelPrediction 6.3.8

• Add support for R4.4
• Fix notes around documentation (vignette engine and brackets in itemize)
• Use webp image format where possible (not in pdfs) for smaller size
• Make sure random table names are unique in tests
• Remove remote info for Eunomia since it’s in CRAN

PatientLevelPrediction 6.3.7

• Clean up dependencies, tibble removed and IHT and ParallelLogger from CRAN
• Use cohortIds for cohortCovariates to comply with FeatureExtraction
• Add cdmDatabaseName from DatabaseDetails to model output
• Fix bug when attributes weren’t preserved on trainData$covariateData after split
• Fix warnings in tests and speed them up
• Fix bug in assignment operator in configurePython
• Delay evaluation of plpData when using do.call like in learningCurves and runMultiplePlp
• Speed up population generation when subjectId’s are distinct
• Fix bug when population was still generated when provided to runPlp

PatientLevelPrediction 6.3.6

• fix bug with ohdsi shiny modules version check (issue 415)

PatientLevelPrediction 6.3.5

• Fix sklearnToJson to be compatible with scikit-learn>=1.3
• Fix github actions so it’s not hardcoded to use python 3.7

PatientLevelPrediction 6.3.4

• added spline feature engineering
• added age/sex stratified imputation feature engineering
• changed result table execution date types to varchar
• updated covariateSummary to use feature engineering

PatientLevelPrediction 6.3.3

• fixed bug introduced with new reticulate update in model saving to json tests

PatientLevelPrediction 6.3.2

• fixed bug with database insert if result is incomplete
• updated/fixed documentation (Egill)
• added model path to models (Henrik)
• updated hyper-parameter saving to data.frame and made consistent

PatientLevelPrediction 6.3.1

• fixed bug with multiple covariate settings in diagnose plp
• added min cell count when exporting database results to csv files
• light GBM added (thanks Jin Choi and Chungsoo Kim)
• fixed minor bugs when uploading results to database

PatientLevelPrediction 6.2.1

• added ensure_installed(“ResultModelManager”) to getDataMigrator()

PatientLevelPrediction 6.1.0

• shiny app is now using ShinyAppBuilder with a config saved in the /inst folder

PatientLevelPrediction 6.0.11

• fixed bugs introduced when sklearn inputs changed
• added sklearn model being saved as jsons
• made changes around the DatabaseConnection get table names function to make it work for the updated DatabaseConnection
• removed check RAM stop (now it just warns)

PatientLevelPrediction 6.0.10

• Updated test to skip test for FE setting if the model does not fit (this was causing occasional test fail)
• replaced .data$ with “” for all dplyr::select to remove warnings

PatientLevelPrediction 6.0.9

• Fix bug with python type being required to be int

PatientLevelPrediction 6.0.8

• Allow priorType to be passed down to getCV function in case prior is not ‘laplace’
• Seed specified in Cyclops model wasn’t passed to Cyclops

PatientLevelPrediction 6.0.7

• fixed issue with shiny viewer converting connection details to large json

PatientLevelPrediction 6.0.6

• added check for cdmDatabaseId into createDatabaseDetails
• added test for check for cdmDatabaseId into createDatabaseDetails to error when NULL
• removed session$onSessionEnded(shiny::stopApp) from shiny server

PatientLevelPrediction 6.0.5

• fixing cox predictions

PatientLevelPrediction 6.0.4

• forcing cdmDatabaseId to be a string if integer is input

PatientLevelPrediction 6.0.3

• replaced utils::read.csv with readr::read_csv when inserting results from csv

PatientLevelPrediction 6.0.2

• replaced gsub with sub when inserting csvs to database

PatientLevelPrediction 6.0.1

• saved result specification csv in windows to fix odd formating issue

PatientLevelPrediction 6.0.0

• fixed sample data bugs
• updated to use v1.0.0 of OhdsiShinyModules
• updated plp database result tables to use the same structure for cohort and database as other HADES packages
• added function to insert csv results into plp database result tables
• added input for databaseId (database and version) when extracting data to be consistent with other HADES packages. This is saved in plp objects.

PatientLevelPrediction 5.4.4

• fixed issue with ‘preprocess’ vs ‘preprocessing’ inconsistently used across models
• added metaData tracking for feature engineering or preprocessing when predicting
• fixed issue with FE using trainData$covariateData metaData rather than trainData
• fixed bug when using sameData for FE

PatientLevelPrediction 5.4.3

• pulled in multiple bug fixes and test improvements from Egill
• pulled in fix for learning curves from Henrik
• Pulled in fix for feature engineering from Solomon
• Cleaned check messages about comparing class(x) with a string by changing to inherits()

PatientLevelPrediction 5.4.2

• removed json saving for sklearn models since sklearn-json is no longer working for the latest sklearn

PatientLevelPrediction 5.4.1

• renamed the input corresponding to the string that gets appended to the results table names to tablePrefix
• fixed issues with system.file() from SqlRender code breaking the tests
• added an input fileAppend to the function that exports the database tables to csv files
• moved the plp model (including preprocessing details) outside of the result database (into a specified folder) due to the size of the objects (too large to insert into the database).

PatientLevelPrediction 5.4.0

• added saving of plp models into the result database
• added default cohortDefinitions in runMultiplePlp

PatientLevelPrediction 5.3.3

• added modelType to all models for database upload

PatientLevelPrediction 5.3.2

• moved FeatureExtraction to depends
• fixed using inherits()

PatientLevelPrediction 5.3.1

• moved most of the shiny app code into OhdsiShinyModules
• removed shiny dependencies and added OhdsiShinyModules to suggests
• fixed bug with linux sklearn saving

PatientLevelPrediction 5.1.1

• replaced cohortId to targetId for consistency throughout code

PatientLevelPrediction 5.1.0

• replaced targetId in model design to cohortId for consistency throughout code
• replaced plpDataSettings to restrictPlpDataSettings to improve naming consistency
• added ability to use initial population in runPlp by adding the population to plpData$population
• added splitSettings into modelDesign
• replaced saving json settings with ParallelLogger function
• updated database result schema (removed researcher_id from tables - if desired a new table with the setting_ids and researcher_id could be added, removed study tables and revised results table to performances table with a reference to model_design_id and development_database_id to enable validation results without a model to be inserted)
• added diagnostic code based on PROBAST
• added diagnostic shiny module
• added code to create sqlite database and populate in uploadToDatabase
• add code to convert runPlp+val to sqlite database when viewing shiny
• added code to extract database results into csv files: extractDatabaseToCsv()

PatientLevelPrediction 5.0.5

• pulled in GBM update (default hyper-parameters and variable importance fix) work done by Egill (egillax)

PatientLevelPrediction 5.0.4

• updated installation documents
• added tryCatch around plots to prevent code stopping

PatientLevelPrediction 5.0.3

• updated result schema (added model_design table with settings and added attrition table)
• updated shiny app for new database result schema
• removed C++ code for AUC and Rcpp dependency, now using pROC instead as faster
• made covariate summary optional when externally validating

PatientLevelPrediction 5.0.2

• updated json structure for specifying study design (made it friendlier to read)
• includes smooth calibration plot fix - work done by Alex (rekkasa)
• fixed bug with multiple sample methods or feature engineering settings causing invalid error

PatientLevelPrediction 5.0.0

• plpModel now saved as json files when possible
• Updated runPlp to make more modular
• now possible to customise data splitting, feature engineering, sampling (over/under) and learning algorithm
• added function for extracting cohort covariates
• updated evalaution to evaluate per strata (evaluation column)
• updated plpModel structure
• updated runPlp structure
• updated shiny and package to use tidyr and not reshape2
• sklearn learning algorithms share the same fit function
• r learning algorithms share the same fit function
• interface to cyclops code revised
• ensemble learning removed (will be in separate package)
• deep learning removed (will be in DeepPatientLevelPrediction package)

PatientLevelPrediction 4.4.2

• revised toSparseM() to do conversion in one go but check RAM availablility beforehand.
• removed temporal plpData conversion in toSparseM (this will be done in DeepPatientLevelPrediction)

PatientLevelPrediction 4.4.1

• shiny can now read csv results
• objects loaded via loadPlpFromCsv() can be saved using savePlpResult()

PatientLevelPrediction 4.4.0

• added database result storage
• added interface to database results in shiny
• merged in shinyRepo that changed the shiny app to make it modular and added new features
• removed deep learning as this is being added into new OHDSI package DeepPatientLevelPrediction

PatientLevelPrediction 4.3.10

• save xgboost model as json file for transparency
• set connectionDetails to NULL in getPlpData

PatientLevelPrediction 4.3.9

• updated andromeda functions - restrict to pop and tidy covs for speed
• quick fix for GBM survival predicting negative values
• fixed occasional demoSum error for survival models
• updated index creation to use Andromeda function

PatientLevelPrediction 4.3.8

• fixed bug when normalize data is false
• fixed bugs when single feature (gbm + python)
• updated GBM

PatientLevelPrediction 4.3.7

• updated calibration slope
• fixed missing age/gender in prediction
• fixed shiny intercept bug
• fixed diagnostic
• fixed missing covariateSettings in load cvs plp

PatientLevelPrediction 4.3.6

• Removed plpData from evaluation
• Added recalibration into externalVal
• Updated shiny app for recalibration
• Added population creation setting to use cohortEndDate as timeAtRisk end
• fixed tests

PatientLevelPrediction 4.3.3

• Reduced imports by adding code to install some dependencies when used

PatientLevelPrediction 4.3.2

• fixed csv result saving bug when no model param

PatientLevelPrediction 4.3.1

• fixed r check vignette issues
• added conda install to test

PatientLevelPrediction 4.3.0

• finalised permutation feature importance

PatientLevelPrediction 4.2.10

• fixed deepNN index issue (reported on github - thanks dapritchard)
• add compression to python pickles
• removed requirement to have outcomeCount for prediction with python models

PatientLevelPrediction 4.2.9

• cleaned all checks
• fixed bug in python toSparseMatrix
• fixed warning in studyPop

PatientLevelPrediction 4.2.8

• fixed bug (identified by Chungsoo) in covariateSummary
• fixed bug with thresholdSummary
• edited threshold summary function to make it cleaner
• added to ensemble where you can combine multiple models into an ensemble
• cleaned up the notes and tests
• updated simulated data covariateId in tests to use integer64
• fixed description imports (and sorted them)

PatientLevelPrediction 4.2.7

• fixed Cox model calibration plots
• fixed int64 conversion bug

PatientLevelPrediction 4.2.6

• added baseline risk to Cox model

PatientLevelPrediction 4.2.3

• updated shiny: added attrition and hyper-parameter grid search into settings

PatientLevelPrediction 4.2.2

• updated shiny app added 95% CI to AUC in summary, size is now complete data size and there is a column valPercent that tells what percentage of the data were used for validation

PatientLevelPrediction 4.2.1

• updated GBMsurvival to use survival metrics and c-stat

PatientLevelPrediction 4.2.0

• added survival metrics

PatientLevelPrediction 4.1.0

• added updates and fixes into master from development branch

PatientLevelPrediction 4.0.6

• fixed bug with pdw data extraction due to multiple person_id columns
• fixed bug in shiny app converting covariate values due to tibble

PatientLevelPrediction 4.0.5

• added calibration updates: cal-in-large, weak cal
• updated smooth cal plot (sample for speed in big data)
• defaulted to 100 values in calibrationSummary + updated cal plot

PatientLevelPrediction 4.0.4

• fixed backwards compat with normalization
• fixed python joblib dependancy

PatientLevelPrediction 4.0.2

• fixed bug in preprocessing
• added cross validation aucs to LR, GBM, RF and MLP
• added more settings into MLP
• added threads option in LR

PatientLevelPrediction 4.0.1

• fixed minor bug with shiny dependency
• fixed some tests
• added standardizedMeanDiff to covariatesummary
• updated createStudyPopulation to make it cleaner to read and count outcome per TAR

PatientLevelPrediction 4.0.0

• Andromeda replaced ff data objects
• added age/gender into cohort
• fixed python warnings
• updated shiny plp viewer

PatientLevelPrediction 3.0.16

• Fixed bug when running multiple analyses using a data extraction sample with multiple covariate settings

PatientLevelPrediction 3.0.15

• improved shiny PLP viewer
• added diagnostic shiny viewer

PatientLevelPrediction 3.0.14

• updated external validate code to enable custom covariates using ATLAS cohorts
• fixed issues with startAnchor and endAnchor

PatientLevelPrediction 3.0.13

• Deprecating addExposureDaysToStart and addExposureDaysToEnd arguments in createStudyPopulation, adding new arguments called startAnchor and endAnchor. The hope is this is less confusing.
• fixed transfer learning code (can now transfer or fine-tune model)
• made view plp shiny apps work when some results are missing

PatientLevelPrediction 3.0.12

• set up testing
• fixed build warnings

PatientLevelPrediction 3.0.11

• added tests to get >70% coverage (keras tests too slow for travis)
• Fixed minor bugs
• Fixed deep learning code and removed pythonInR dependancy
• combined shiny into one file with one interface

PatientLevelPrediction 3.0.10

• added recalibration using 25% sample in existing models
• added option to provide score to probabilities for existing models
• fixed warnings with some plots

PatientLevelPrediction 3.0.9

Small bug fixes: - added analysisId into model saving/loading - made external validation saving recursive - added removal of patients with negative TAR when creating population - added option to apply model without preprocessing settings (make them NULL) - updated create study population to remove patients with negative time-at-risk

PatientLevelPrediction 3.0.8

Changes: - merged in bug fix from Martijn - fixed AUC bug causing crash with big data - update SQL code to be compatible with v6.0 OMOP CDM - added save option to external validate PLP

PatientLevelPrediction 3.0.7

Changes: - Updated splitting functions to include a splitby subject and renamed personSplitter to randomSplitter - Cast indices to integer in python functions to fix bug with non integer sparse matrix indices

PatientLevelPrediction 3.0.5

Changes: - Added GLM status to log (will now inform about any fitting issue in log) - Added GBM survival model (still under development) - Added RF quantile regression (still under development) - Updated viewMultiplePlp() to match PLP skeleton package app - Updated single plp vignette with additional example - Merge in deep learning updates from Chan

PatientLevelPrediction 3.0.4

Changes: - Updated website

PatientLevelPrediction 3.0.3

Changes: - Added more tests - test files now match R files

PatientLevelPrediction 3.0.2

Changes: - Fixed ensemble stacker

PatientLevelPrediction 3.0.1

Changes: - Using reticulate for python interface - Speed improvements - Bug fixes