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Add Survival Mixture Density Networks for survival prediction

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#1,297 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
30/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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.

Lenguaje dominante
Python
Estrellas
1.7k
Forks
803
Merge medio
5 d 20 h
PR fusionados (30 d)
56

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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