{"id":23111739,"url":"https://github.com/arturomoncadatorres/deepsurvk","last_synced_at":"2025-08-16T19:30:50.789Z","repository":{"id":38093999,"uuid":"280457255","full_name":"arturomoncadatorres/deepsurvk","owner":"arturomoncadatorres","description":"Implementation of DeepSurv using Keras","archived":false,"fork":false,"pushed_at":"2023-07-06T22:01:18.000Z","size":2576,"stargazers_count":50,"open_issues_count":14,"forks_count":18,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-12-16T00:49:34.827Z","etag":null,"topics":["data-science","deep-learning","keras","survival-analysis","tensorflow2"],"latest_commit_sha":null,"homepage":"https://deepsurvk.readthedocs.io/en/latest/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arturomoncadatorres.png","metadata":{"files":{"readme":"README.md","changelog":"HISTORY.rst","contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-07-17T15:15:47.000Z","updated_at":"2024-12-14T06:02:34.000Z","dependencies_parsed_at":"2023-01-31T02:00:58.990Z","dependency_job_id":null,"html_url":"https://github.com/arturomoncadatorres/deepsurvk","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arturomoncadatorres%2Fdeepsurvk","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arturomoncadatorres%2Fdeepsurvk/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arturomoncadatorres%2Fdeepsurvk/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arturomoncadatorres%2Fdeepsurvk/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arturomoncadatorres","download_url":"https://codeload.github.com/arturomoncadatorres/deepsurvk/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230051322,"owners_count":18165199,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","deep-learning","keras","survival-analysis","tensorflow2"],"created_at":"2024-12-17T02:10:12.725Z","updated_at":"2024-12-17T02:10:13.207Z","avatar_url":"https://github.com/arturomoncadatorres.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/arturomoncadatorres/deepsurvk/blob/master/docs/artwork/logo.png?raw=true\" width=\"500\" /\u003e\n\u003c/p\u003e\n\n\u003ch3 align=center\u003e Implementation of DeepSurv using Keras\u003c/h3\u003e\n\n\u003ch3 align=\"center\"\u003e\n\n[![PyPI version](https://badge.fury.io/py/deepsurvk.svg)](https://badge.fury.io/py/deepsurvk)\n[![Build Status](https://img.shields.io/travis/arturomoncadatorres/deepsurvk.svg?branch=master)](https://travis-ci.org/arturomoncadatorres/deepsurvk)\n[![Documentation](https://readthedocs.org/projects/deepsurvk/badge/?version=latest)](https://deepsurvk.readthedocs.io/en/latest/?badge=latest)\n[![PyUp](https://pyup.io/repos/github/arturomoncadatorres/deepsurvk/shield.svg)](https://pyup.io/repos/github/arturomoncadatorres/deepsurvk/)\n\n\u003c/h3\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#pray-motivation\"\u003eMotivation\u003c/a\u003e •\n  \u003ca href=\"#tada-features\"\u003eFeatures\u003c/a\u003e •\n  \u003ca href=\"#bookmark_tabs-documentation\"\u003eDocumentation\u003c/a\u003e •\n  \u003ca href=\"#page_with_curl-license\"\u003eLicense\u003c/a\u003e •\n  \u003ca href=\"#black_nib-references\"\u003eReferences\u003c/a\u003e •\n  \u003ca href=\"#label-credits\"\u003eCredits\u003c/a\u003e\n\u003c/p\u003e\n\n---\n## :pray: Motivation\n\nDeepSurv is a Cox Proportional Hazards deep neural network used for modeling interactions between a patient's covariates and treatment effectiveness. It was originally proposed by [Katzman et. al (2018)](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-018-0482-1) and [implemented in Theano (using Lasagne)](https://github.com/jaredleekatzman/DeepSurv).\n\nUnfortunately, [Theano is no longer supported](https://groups.google.com/forum/#!msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ). There have been some attempts in recreating DeepSurv in other DL platforms, such as [czifan's `DeepSurv.pytorch`](https://github.com/czifan/DeepSurv.pytorch). However, given its popularity and ease of use, I think TensorFlow 2's Keras is a great option for this task.\n\n[mexchy1000 created `DeepSurv_Keras`](https://github.com/mexchy1000/DeepSurv_Keras). However, it is a very raw prototype: it is not properly documented nor validated. Moreover, it is not being actively supported anymore. Therefore, I used it as a rough starting point for the development of DeepSurvK.\n\nThis is my first Python package. I am sure there are many places where it could be improved. Feedback is always welcome!\n\n## :tada: Features\n* Implemented using Keras (using TensorFlow 2)\n* Includes the original datasets together with a proper description of the variables\n* Designed with data as pandas DataFrames in mind\n* Visualization tools for the most common plots for fast and easy exploration and prototyping\n* Treatment recommender\n* (Basic) hyperparameter optimization using grid and randomized search\n\n## :bookmark_tabs: Documentation\nYou can find the complete package's documentation [here](https://deepsurvk.readthedocs.io). Unfortunately, I haven't had as much time as I would like to work on it. Alternatively, I strongly recommend you take look at the [example notebooks](https://github.com/arturomoncadatorres/deepsurvk/tree/master/examples).\n\n## :page_with_curl: License\nThis package uses the MIT license\n\n## :black_nib: References\nIf you are using DeepSurvK, please cite the original DeepSurv paper, as well as the current repository as follows:\n\n\u003e * Katzman, Jared L., et al. [\"DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.\"](https://link.springer.com/article/10.1186/s12874-018-0482-1) BMC medical research methodology 18.1 (2018): 24. [[BibTeX](https://scholar.googleusercontent.com/scholar.bib?q=info:hG13Z0IGDPkJ:scholar.google.com/\u0026output=citation\u0026scisdr=CgXVK4mOEOOa6e7oHyc:AAGBfm0AAAAAXxbtByd6uXB8fbxpWDom9eCJp71TAtUO\u0026scisig=AAGBfm0AAAAAXxbtB35QPVsdnSAHsADGSX408btb6Gvf\u0026scisf=4\u0026ct=citation\u0026cd=-1\u0026hl=en)]\n\u003e * Arturo Moncada-Torres. DeepSurvK. Accessed on [MONTH, 20XX].\n\n## :label: Credits\nThis package was developed in [Spyder](https://www.spyder-ide.org/) (a fantastic open-source Python IDE) using [Cookiecutter](https://github.com/cookiecutter/cookiecutter) and the [`arturomoncadatorres/cookiecutter-pypackage` project template](https://github.com/arturomoncadatorres/cookiecutter-pypackage).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farturomoncadatorres%2Fdeepsurvk","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farturomoncadatorres%2Fdeepsurvk","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farturomoncadatorres%2Fdeepsurvk/lists"}