library_name: keras
tags:
- healthcare
- icd-10
- mortality-prediction
- readmission-prediction
- deepset
- tabular
NRD ICD-10 outcome models
DeepSet + (optional) Transformer models trained on the National Readmission Database (NRD, 2016–2020) to predict two outcomes from up to 40 ICD-10 diagnosis codes plus demographics (AGE, FEMALE, PAY1, ZIPINC_QRTL).
| Subfolder | Outcome | Description |
|---|---|---|
mortality_30day/ |
MOR30 |
30-day post-discharge mortality |
readmission_30day/ |
REA30 |
30-day all-cause readmission |
encoders/ holds the fitted LabelEncoder (ICD-10 → integer IDs) and
MinMaxScaler (AGE) used at training time. Inputs must be encoded with the
same artifacts at inference, or predictions will be meaningless.
Loading a model
The .keras files contain three custom serializable components
(DeepSet, TransformerBlock, F2Score) that must be importable (and
registered via @tf.keras.utils.register_keras_serializable(package="Custom"))
before load_model:
import tensorflow as tf
from huggingface_hub import hf_hub_download
# Register your custom classes — see src/train/ in the source repo
from custom_layers import DeepSet, TransformerBlock, F2Score # noqa: F401
path = hf_hub_download(
repo_id="<user-or-org>/<repo-name>",
filename="mortality_30day/mort_hypertrial_auc.keras",
)
model = tf.keras.models.load_model(path)
Variants
Within each outcome subfolder, file suffixes denote the architecture:
_hypertrial_auc— best model from the Keras-Tuner search (recommended)_icd_only— ICD codes only, no demographics (ablation)_no_deepset— flattened ICD input, no DeepSet aggregation (ablation)_with_transformers/_transformer— DeepSet + TransformerBlocks
Data restrictions
NRD is a HCUP product distributed under a Data Use Agreement. These weights do not contain individual records, but downstream users should be aware of the source.