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RepNbrsOutlierCleaner

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Wikimedial
Infrastructure C Library
Installation
Medial Tools
Models
Python
Repositories
Research

Medial EarlySign Documentation

Wikimedial
Infrastructure C Library
Infrastructure C Library
- Infrastructure Application Version
- MedModel JSON Format
- 00.InfraMed Library page
  00.InfraMed Library page
- 01.Rep Processors Practical Guide
  01.Rep Processors Practical Guide
  - Cleaners Json Examples
  - Full Rep Processor
  - History Limit repo processor
  - How to print, how many outliers were
  - How to remove signal during admissions
  - How to rename signal
  - How to Write a RepProcessor
  - Noiser
  - Rep Calculator
  - Virtual Signals
  - How to create an empty signal
    
    How to create an empty signal
    
    Howto create a signal with constant value
- 02.Feature Generator Practical Guide
  02.Feature Generator Practical Guide
  - BasicFeatGenerator
  - How to Write a Feature Generator
- 03.FeatureProcessor practical guide
  03.FeatureProcessor practical guide
  - Charlson
  - Framingham Feature Processor
  - How to Write a Feature Processor
  - Embeddings
    
    Embeddings
    
    Embeddings WalkThrough Example
- 04.MedAlgo Library
  04.MedAlgo Library
  - Feature Importance
  - Machine Learning Algorithms - Parameter Tuning (C++ Code)
  - but_why - feature contribs
  - MedPredictor practical guide
    
    MedPredictor practical guide
    
    How to Write a MedPredictor
    
    MASK predictor - predict by_missing_value_subset
    
    XGBoost added features
- 05.PostProcessors Practical Guide
  05.PostProcessors Practical Guide
- AlgoMarkers
  AlgoMarkers
- MedProcessTools Library
  MedProcessTools Library
  - MedBootstrap
  - MedCohort
  - MedFeatures
  - MedLabels
  - MedModel
  - MedPlot
  - MedSamples
  - SerializableObject
  - FeatureGenerator
    
    FeatureGenerator
    
    ModelFeatGenerator
    
    Unified Smoking Feature Generator
    
    Unified Smoking Feature Generator
    
    Code Documnetation
  - FeatureProcessor
    
    FeatureProcessor
    
    FeatureSelector
    
    FeatureEncoder
    
    FeatureEncoder
    
    FeaturePCA
  - MedRegistry
    
    MedRegistry
    
    MedSamplingStrategy
    
    TimeWindowInteraction
  - RepProcessor
    
    RepProcessor
    
    MedValueCleaner
    
    RepAggregationPeriod
    
    RepBasicOutlierCleaner
    
    RepBasicRangeCleaner
    
    RepConfiguredOutlierCleaner
    
    RepMultiProcessor
    
    RepNbrsOutlierCleaner RepNbrsOutlierCleaner
    Table of contents
    
    RepProcessor applies in-place processing to the information held for a single patient-id (a DynamicPidRec)
    
    RepRulebasedOutlierCleaner
  - SampleFilter
    
    SampleFilter
    
    BasicSampleFilter
    
    BasicTestFilter
    
    BasicTrainFilter
    
    MatchingSampleFilter
    
    OutlierSampleFilter
Installation
Installation
Medial Tools
Medial Tools
- Compare AUC's
- Doxygen
- Fairness Extraction
- Howto Debug Program in Linux
- Iterative Feature Selector
- Optimizer
- Simulator
- TestModelExternal
- adjust_model
- model_signals_importance
- Deprecated
  Deprecated
  - Find Required Signals
  - SignalsDependencies
  - action_outcome_effect
  - GANs for imputing matrices
    
    GANs for imputing matrices
    
    TrainingMaskedGAN
  - Create registry
    
    Create registry
    
    Create Membership registry example command
- Guide for common actions
  Guide for common actions
- Model Checklist
  Model Checklist
  - AutoTest
    
    AutoTest
    
    Development kit
    
    Development kit
    
    Test_01 - test_train_samples_over_years
    
    Test_02 - test samples
    
    Test_03 - test cleaners
    
    Test_04 - test imputers
    
    Test_05 - But why
    
    Test_06 - bootstrap results
    
    Test_07 - feature importance
    
    Test_08 - calibration
    
    Test_09 - coverage
    
    Test_10 - test matrix features
    
    Test_11 - test matrix over years
    
    Test_12 - fairness
    
    Test_13 - model explainability
    
    Test_14 - noise sensitivity analysis
    
    Test_15 - compare to baseline model
    
    External Silent Run
    
    External Silent Run
    
    TODO
    
    Test 1 - Generate Repository
    
    Test 10/12 - butwhy (TBD)
    
    Test 11 - Sample Dates
    
    Test 13 - Features and Flagged (TBD)
    
    Test 2 - Compare Repository with Reference Matrix
    
    Test 3 & 4 - Compare Score Distribution
    
    Test 5 - Sex Ratio
    
    Test 6 - Special Groups
    
    Test 7 - Compare Important Feature
    
    Test 8 - Estimate Performances
    
    Test 9 - Lab Frequency
    
    Tips and Scripts
- Using the Flow App
  Using the Flow App
- Bootstrap app
  Bootstrap app
  - Bootstrap legend
  - Utility tools to process bootstrap results
  - Extending bootstrap
    
    Extending bootstrap
    
    Using Harrell C Statistics
- Change model
  Change model
  - Limiting Memory Usage in predict
Models
Models
Python
Python
Repositories
Repositories
- Loading a New Repository
- Medical vocabulary mappings
- Repository Signals File Format
- Repository Viewers
- Solution details ETL process tool
  Solution details ETL process tool
  - ETL_process TODO
  - ETL Tutorial
    
    ETL Tutorial
    
    ETL Process – Dynamic Testing of Signals
    
    00.Setup
    
    00.Setup
    
    01.Data Fetching step
    
    01.Data Fetching step
    
    02.Process Pipeline
    
    02.Process Pipeline
    
    Categorical Signals & Custom Dictionaries
    
    Unit conversion
    
    Unit conversion
    
    Full code example
    
    Example config/output file of unit conversion
    
    03.Finalize Load
    
    03.Finalize Load
  - High level important paths
    
    High level important paths
    
    📁 CODE_DIR: Your ETL Workspace
    
    📂 ETL_INFRA_DIR: A Closer Look
    
    📁 WORK_DIR: The Output Directory
Research
Research
- Factorization Machines
- Journal Club Page
- Missing Values Based Uncertainty Analysis
- Raindrop
- Right Censoring
- But Why Explainers
  But Why Explainers
  - Experiments - Stage B
  - Experiments - Stage C (Freeze Version 1)
  - Sanity test experiment for debuging
  - ButWhy experiments results
    
    ButWhy experiments results
    
    TestGibbs
- Conferences
  Conferences
- What If
  What If

Table of contents

RepProcessor applies in-place processing to the information held for a single patient-id (a DynamicPidRec)

RepNbrsOutlierCleaner

RepProcessor applies in-place processing to the information held for a single patient-id (a DynamicPidRec)

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