NEWS.md

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

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

OHDSI/PatientLevelPrediction documentation built on May 3, 2024, 12:11 a.m.