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Add Survival Mixture Density Networks for survival prediction

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难度
4/5
预计耗时
3-5 天
新手友好度
30/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
活跃
技术栈
python
领域
machine-learning

调研方向

Start by reading the existing BaseModel and the trainer's custom-loss support, since the issue plans to build on both. The first step is a SurvivalMDN model with a right-censored likelihood loss, checked against the authors' implementation on controlled inputs. Done means the model's loss and predictions at chosen times are tested, then the SUPPORT2 task and an end-to-end example are added. Maintainer feedback on scope is requested first, so confirm the plan before writing code.

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描述

We’d like to contribute an implementation of Survival Mixture Density Networks (Han et al., 2022) to PyHealth as part of a UIUC course project.

We propose adding a reusable Survival MDN model for continuous-time survival prediction with right-censored data. The model would accept patient features, observed follow-up times, and event indicators from compatible datasets. A dataset-specific SUPPORT2 task and an end-to-end example would demonstrate its use.

We plan to add:

  • A SurvivalMDN model extending BaseModel, with a censored likelihood loss and survival predictions at user-selected times.
  • Patient-specific, learned shared, and uniform mixture-weight options.
  • A SUPPORT2 time-to-event task.
  • Tests, documentation, and a training/evaluation example.

We’ll validate loss values and predictions against the authors’ implementation on controlled inputs and use their released SUPPORT benchmark for our reproduction. We’ll write an original implementation based on the paper, with attribution.

For training, we plan to use the existing trainer’s custom-loss support and add a separate survival evaluation utility. We’ll develop this in small, tested steps, starting with the model and then adding the SUPPORT2 task and evaluation example.

We’d welcome feedback on the proposed scope or any overlapping work.

主要语言
Python
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5 天 16 小时
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环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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