Deep Survival Machines - Fully Parametric Survival Regression

Overview

Build Status     codecov     License: MIT     GitHub Repo stars

Package: dsm

Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The underlying model is implemented in pytorch.

For full documentation of the module, please see https://autonlab.github.io/DeepSurvivalMachines/

What is Survival Analysis?

Survival Analysis involves estimating when an event of interest, T would take place given some features or covariates X. In statistics and ML, these scenarios are modelled as regression to estimate the conditional survival distribution, P(T>t|X).
As compared to typical regression problems, Survival Analysis differs in two major ways:

  • The Event distribution, T has positive support i.e. T ∈ [0, ∞).
  • There is presence of censoring i.e. a large number of instances of data are lost to follow up.

Deep Survival Machines

Deep Survival Machines (DSM) is a fully parametric approach to model Time-to-Event outcomes in the presence of Censoring, first introduced in [1]. In the context of Healthcare ML and Biostatistics, this is known as 'Survival Analysis'. The key idea behind Deep Survival Machines is to model the underlying event outcome distribution as a mixure of some fixed ( K ) parametric distributions. The parameters of these mixture distributions as well as the mixing weights are modelled using Neural Networks.

Usage Example

from dsm import DeepSurvivalMachines
model = DeepSurvivalMachines()
model.fit()
model.predict_risk()

Recurrent Deep Survival Machines

Recurrent Deep Survival Machines (RDSM) builds on the original DSM model and allows for learning of representations of the input covariates using Recurrent Neural Networks like LSTMs, GRUs. Deep Recurrent Survival Machines is a natural fit to model problems where there are time dependendent covariates.

Deep Convolutional Survival Machines

Predictive maintenance and medical imaging sometimes requires to work with image streams. Deep Convolutional Survival Machines extends DSM and DRSM to learn representations of the input image data using convolutional layers. If working with streaming data, the learnt representations are then passed through an LSTM to model temporal dependencies before determining the underlying survival distributions.

⚠️ Not Implemented Yet!

Deep Cox Mixtures

The Cox Mixture involves the assumption that the survival function of the individual to be a mixture of K Cox Models. Conditioned on each subgroup Z=k; the PH assumptions are assumed to hold and the baseline hazard rates is determined non-parametrically using an spline-interpolated Breslow's estimator. For full details on Deep Cox Mixture, refer to the paper:

Deep Cox Mixtures for Survival Regression. Machine Learning in Health Conference (2021)

Installation

[email protected]:~$ git clone https://github.com/autonlab/DeepSurvivalMachines.git
[email protected]:~$ cd DeepSurvivalMachines
[email protected]:~$ pip install -r requirements.txt

Examples

  1. Deep Survival Machines on the SUPPORT Dataset
  2. Recurrent Deep Survival Machines on the PBC Dataset

References

Please cite the following papers if you are using the dsm package.

[1] Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks. IEEE Journal of Biomedical & Health Informatics (2021)

  @article{nagpal2021deep,
  title={Deep Survival Machines: Fully Parametric Survival Regression and\
  Representation Learning for Censored Data with Competing Risks},
  author={Nagpal, Chirag and Li, Xinyu and Dubrawski, Artur},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2021}
  }

[2] Deep Parametric Time-to-Event Regression with Time-Varying Covariates. AAAI Spring Symposium (2021)

@InProceedings{pmlr-v146-nagpal21a,
  title = 	 {Deep Parametric Time-to-Event Regression with Time-Varying Covariates},
  author =       {Nagpal, Chirag and Jeanselme, Vincent and Dubrawski, Artur},
  booktitle = 	 {Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
  }

[3] Deep Cox Mixtures for Survival Regression. Machine Learning for Healthcare (2021)

@InProceedings{nagpal2021dcm,
  title={Deep Cox Mixtures for Survival Regression},
  author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
  booktitle={Proceedings of the 6th Machine Learning for Healthcare Conference},
  pages={674--708},
  year={2021},
  volume={149},
  series={Proceedings of Machine Learning Research},
  publisher={PMLR},
}

Compatibility

dsm requires python 3.5+ and pytorch 1.1+.

To evaluate performance using standard metrics dsm requires scikit-survival.

Contributing

dsm is on GitHub. Bug reports and pull requests are welcome.

License

MIT License

Copyright (c) 2020 Carnegie Mellon University, Auton Lab

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Owner
Carnegie Mellon University Auton Lab
Carnegie Mellon University Auton Lab
Machine Learning Algorithms ( Desion Tree, XG Boost, Random Forest )

implementation of machine learning Algorithms such as decision tree and random forest and xgboost on darasets then compare results for each and implement ant colony and genetic algorithms on tsp map,

Mohamadreza Rezaei 1 Jan 19, 2022
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.

7.4k Jan 04, 2023
CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

Zelros 67 Dec 28, 2022
Forecast dynamically at scale with this unique package. pip install scalecast

🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels

Michael Keith 158 Jan 03, 2023
Markov bot - A Writing bot based on Markov Chain for Data Structure Lab

基于马尔可夫链的写作机器人 前端 用html/css完成 Demo展示(已给出文本的相应展示) 用户提供相关的语料库后训练的成果 后端 要完成的几个接口 解析文

DysprosiumDy 9 May 05, 2022
Hierarchical Time Series Forecasting using Prophet

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Collin Rooney 131 Dec 02, 2022
Combines Bayesian analyses from many datasets.

PosteriorStacker Combines Bayesian analyses from many datasets. Introduction Method Tutorial Output plot and files Introduction Fitting a model to a d

Johannes Buchner 19 Feb 13, 2022
Library of Stan Models for Survival Analysis

survivalstan: Survival Models in Stan author: Jacki Novik Overview Library of Stan Models for Survival Analysis Features: Variety of standard survival

Hammer Lab 122 Jan 06, 2023
K-Means clusternig example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se

Emin 1 Dec 13, 2021
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

Shigang Li 6 Jun 18, 2022
Time-series momentum for momentum investing strategy

Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an

Victor Caldeira 3 Jun 18, 2022
Decision Tree Regression algorithm implemented on Python from scratch.

Decision_Tree_Regression I implemented the decision tree regression algorithm on Python. Unlike regular linear regression, this algorithm is used when

1 Dec 22, 2021
The code from the Machine Learning Bookcamp book and a free course based on the book

The code from the Machine Learning Bookcamp book and a free course based on the book

Alexey Grigorev 5.5k Jan 09, 2023
A scikit-learn based module for multi-label et. al. classification

scikit-multilearn scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Pyth

802 Jan 01, 2023
Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Artsem Zhyvalkouski 64 Nov 30, 2022
Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

Keivan Ipchi Hagh 1 Nov 22, 2021
Laporan Proyek Machine Learning - Azhar Rizki Zulma

Laporan Proyek Machine Learning - Azhar Rizki Zulma Project Overview Domain proyek yang dipilih dalam proyek machine learning ini adalah mengenai hibu

Azhar Rizki Zulma 6 Mar 12, 2022
Python library for multilinear algebra and tensor factorizations

scikit-tensor is a Python module for multilinear algebra and tensor factorizations

Maximilian Nickel 394 Dec 09, 2022
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

MosaicML 2.8k Jan 06, 2023