Dynamic-DeepHit for landmark prediction

A fork of Jeanselme/DynamicDeepHit, the PyTorch implementation of Dynamic-DeepHit (Lee et al., IEEE TBME 2020; original TensorFlow code at chl8856/Dynamic-DeepHit).

The fork adapts the model to landmark prediction from irregular clinic visits with a competing risk. At any visit, it predicts the risk of the event of interest within h days, given everything seen so far and given that the patient is still event-free.

What changed

Each row is one or a few commits; docs/modifications.md gives the problem, the change and how it was tested.

Foundation. These change upstream defaults where the upstream behaviour was a bug.

change	where
Port to auton-survival (the maintained successor of DeepSurvivalMachines)	ddh/dsm_compat.py
Discretisation index fix: the earliest events were scored in the last bin	discretize
Training loop: per-epoch shuffle, full validation, LR schedule, gradient clipping, seeding	utils.train_ddh, fit
Ranking loss as in the paper: σ inside the exponential, summed over pairs, per-cause weights	losses.ranking_loss
Likelihood and ranking conditioned on survival to a landmark, plus six fixes from a self-review	fit(s=...), losses

Modifications. Every one is opt-in; with default arguments, predictions are bit-identical to the foundation.

change	switch	problem it addresses
Likelihood at every landmark	all_landmark_nll=True	trained on last visits only, evaluated at every landmark
Discrete-time cause-specific hazard head	hazard=True, proportional=True	the softmax head must spend all probability inside the time grid
Landmark-day decay and between-patient entry weights	landmark_decay_days, entry_weight	where in follow-up the objective puts its weight
Horizon-specific likelihood	gamma, horizon_days	nothing in the bin-level likelihood constrains cumulative risk at h
Landmark row weighting	landmark_row_weighting="visit"/"given"/"band"	patients with early events have few visits, so 1/Vi weighting over-weights them early

The configuration the project reports is hazard=True, all_landmark_nll=True, alpha=0, landmark_row_weighting="band". One experiment that was tried and not adopted (the ranking term at every landmark) is kept on the experiments/all-landmark-rank branch; docs/modifications.md says why it was dropped.

Install

git clone https://github.com/dadudu13/DynamicDeepHit && cd DynamicDeepHit
pip install torch
# auton-survival 0.1.0; --no-deps because its release metadata pins torch<2.0
pip install --no-deps "auton-survival @ git+https://github.com/autonlab/auton-survival@5dde465f7223601717abddc1d075e837707c403b"
pip install -e ".[test,eval]"
python -m pytest

Tested locally with Python 3.11 and torch 2.11 and 2.14; CI runs Python 3.11 and 3.12.

Use

import numpy as np
from ddh import DynamicDeepHit

# x: per-patient arrays (visits x features); t, e: per-visit outcome time (days from the
# first visit) and event (0 censored, 1 event of interest, 2 competing);
# visit_days: per-patient arrays of each visit's day; s: each patient's last visit day.
model = DynamicDeepHit(split=50, hidden_rnn=32, typ="GRU", alpha=0, beta=0.75, hazard=True)
model.fit(x, t, e, iters=50, s=s, visit_days=visit_days,
          all_landmark_nll=True, landmark_row_weighting="band",
          landmark_band_edges=[0, 30, 90, 180, 365, 730])
cif_event = 1 - model.predict_survival(x, [90.0, 180.0], risk=1)   # unconditional, from day 0

To get risks conditioned on a landmark, and the full train / external-cohort / evaluation workflow, see evaluation/. It runs end to end on the synthetic cohort it generates.

How the history is organised

• One change per commit, each explaining the problem it solves. Every commit passes the tests that exist at that point.
• Upstream's history is kept intact underneath. The work is merged through one pull request per theme: foundation, landmark training, hazard head, landmark-time weighting, horizon term, row weighting, packaging, evaluation, documentation.
• Experiments that were not adopted stay on their own branches.

Credit and license

The original PyTorch implementation is by Vincent Jeanselme (MIT); the modifications are by Dulin Wang, under the same license (LICENSE).