Downloads · 30 days
3
5% of all-time downloads
m31-patient-timelines
zinahghulam/m31-patient-timelines
m31-patient-timelines is a tabular classification model from zinahghulam. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for pytorch.
Transformer-based longitudinal EHR model for forecasting newly diagnosed clinical conditions from patient timelines.
All-time downloads
56
Public
Parameters
692K
11.1 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.8 MB · 100%
At a glance
- Task
- Tabular Classification
- Library
- pytorch
- Access
- Public
- Created
- Aug 13, 2026
- Updated
- Aug 14, 2026
- SHA
- 3016b3d5
Model card
From the Hugging Face model README
M31 Patient Timelines
Transformer-based longitudinal EHR model for forecasting newly diagnosed clinical conditions from patient timelines.
Model
- Architecture: EHRTransformer
- Embedding dimension: 128
- Transformer layers: 3
- Attention heads: 4
- Maximum sequence length: 512
- Output: 40-condition multi-label prediction
- Vocabulary: 209 clinical event tokens
- Training: AdamW + Focal Loss with positive-class weighting
- Early stopping based on validation macro-AUROC
The model was trained from scratch using the provided synthetic EHR dataset. No external datasets or pretrained weights were used.
Validation Performance
The following results are from the held-out validation cohort at the optimal early-stopping checkpoint (Epoch 20):
| Metric | Validation |
|---|---|
| Macro AUROC | 0.6642 |
| mAP | 0.1344 |
| Macro F1 | 0.1380 |
| Brier Score | 0.1953 |
The test-set ground truth is withheld for independent scoring.
Code
Source code and reproducibility instructions:
- Task
- Tabular Classification
- Library
- pytorch
- Created
- Aug 13, 2026
- Updated
- Aug 14, 2026