https://github.com/adibender/machine-learning-for-survival-ecml2020

Code repository for "A General Machine Learning Framework for Survival Analysis" published at ECML 2020
https://github.com/adibender/machine-learning-for-survival-ecml2020

gradient-boosting machine-learning survival-analysis

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Code repository for "A General Machine Learning Framework for Survival Analysis" published at ECML 2020

• Host: GitHub
• URL: https://github.com/adibender/machine-learning-for-survival-ecml2020
• Owner: adibender
• Created: 2020-06-22T07:08:40.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-06-26T05:05:08.000Z (over 4 years ago)
• Last Synced: 2024-10-10T18:19:36.246Z (3 months ago)
• Topics: gradient-boosting, machine-learning, survival-analysis
• Language: R
• Homepage:
• Size: 21.5 KB
• Stars: 9
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Overview

This is the code repository for "A General Machine Learning Framework for Survival Analysis" published at ECML 2020

The analyses in the publication were based on a prototype implementation of

Piece-wise-exponential models (PEMs) using XGBoost in R. This prototype implementation

is available as an R package **`pem.xgb`** and must be installed to run the

benchmarks. The package is available from a [separate repository](https://github.com/adibender/pem.xgb). If you are only interested in how to make XGBoost

estimate PEMs head over to that repository. Note that the package is a prototype and should not be used in production. In the near future we are planing to integrate the general framework, including GBT (PEM) into **`mlr3`** via **`mlr3proba`** and

**`mlr3pipelines`**.

If you experience any problems or need advice on how to run the models, don't

hesitate to open an issue or contact the first author.

# Benchmark experiments

To run the code for the benchmark experiments you first need to install

two packages:

```r

# install pem.xgb

devtools::install_github("adibender/pem.xgb") # PEM via XGBoost

devtools::install_github("adibender/pammtools", ref = "ecml")# Data trafo

```

## Folder structure

- Code for benchmarks based on real data sets is contained within folder

**`benchmark`**

  - `prep_data.R`: preprocesses the different data sets used for benchmarking

  - `problems.R`: contains functions that generate an instances during each

  iteration of the benchmark

  - `algorithms.R`: contains wrapper functions for the different algorithms that

  - are compared to each other

  - `deep_c_index.R`: an R version of the C-index as implemented in the DeepHit package

  - `calling_deephit.R`: an R version of the DeepHit package wrapped in a random search function

  - `benchmark-deephit.R`: contains the code to perform the random search for DeepHit 

  on all benchmark data sets

  - `benchmark.R`: contains the code that will performs the benchmark experiments

  and is based on package `batchtools`.

  - `bench-time.R`: benchmark w.r.t. different strategies w.r.t. cut-point selection (scaling experiment)

  - `benchmark-metabric.R`: benchmarks for `metabric` data set for comparison with DeepHit

  - `benchmark-pbc-tvf`: benchmark of the PBC data sets (with time-dependent covariates)

  - `benchmark-mgus2.CR`: benchmark of the MGUS data with competing risks for

  comparison with DeepHit

  - `extract-benchmark-instances.R` script that extracts results from benchmark

  as well as instances to facilitate comparison with DeepHit

- Code for benchmarks based on synthetic data sets is contained within

folder **`simulation`**

  - `sim_funs.R` contains functions that simulate data for right-censored and

  competing risks data

  - `tv-effects.R`: contains code to run benchmark w.r.t. to performance in presence of time-varying effects/non-proportional hazards (right-censored and competing

  risks)

- Code for the aggregation and evaluation of benchmark studies are contained in folder **`paper`**

  - `results.R`: reads in the results from different experiments, combines results, creates raw tables (manually processed for publication)

## Usage of DeepHit wrapper in R

In order to use DeepHit in R, clone the [DeepHit repository](https://github.com/chl8856/DeepHit) into the benchmark folder. The `calling_deephit.R` wrapper that calls DeepHit from R needs a suitable Python environment (Python 2.7) and will import the functions `class_DeepHit.py`, `import_data.py` and

`utils_eval.py`.