Cadence — Next Clinical Event Prediction

cadence-core is a pretrained neural model for next clinical event prediction from electronic health record (EHR) sequences. Given a patient's longitudinal clinical history, it predicts which of 48 clinical event categories will occur next and how many days until that event.

Model Description

Cadence implements the Narrative Velocity Composite (NV-C) framework — a 5.86M parameter residual MLP that fuses structured EHR features with PubMedBERT cluster-semantic embeddings under self-knowledge distillation.

Component	Details
Architecture	3-block residual MLP with LayerNorm
Input dimension	2,420 (884 NV features + 768 PubMedBERT mean + 768 PubMedBERT last)
Classification head	Linear → 48 event-class logits
Regression head	Linear → 19-bin discretized time-to-event
Parameters	5.86M
Training	Cross-entropy + ordinal regression + self-knowledge distillation

Performance

Trained and evaluated on MIMIC-IV v3.1 (100k training tier, male cohort):

Model	Top-1 Accuracy	95% CI	MAE (days)	95% CI
Cadence (NV-C)	34.18%	[33.84%, 34.42%]	36.95	[36.10, 37.68]
XGBoost-884	32.35%	—	38.58	—
Majority-class	9.25%	—	—	—
Random	2.08%	—	—	—

Bootstrap 95% CI: N=105,968 test instances, 2,000 resamples. XGBoost falls outside Cadence's CI on both metrics.

Key finding: Self-knowledge distillation yields a disproportionately large top-1 gain (+0.81 pp) when applied after PubMedBERT cluster-semantic fusion — compared to ~0 pp gain from self-KD on structured features alone — suggesting a genuine interaction between frozen semantic embeddings and knowledge distillation.

Installation & Usage

pip install cadence-core

import torch
from cadence import CadenceModel, load_checkpoint

model = CadenceModel()
load_checkpoint(model)
model.eval()

# Input: 2420-dimensional feature vector
# [0:884]    — 884 Narrative Velocity (NV) clinical features
# [884:1652] — 768-dim PubMedBERT mean-pooled cluster-semantic embedding
# [1652:2420] — 768-dim PubMedBERT last-token cluster-semantic embedding
x = torch.randn(1, 2420)

with torch.no_grad():
    logits, time_bins = model(x)

event_probs = torch.softmax(logits, dim=-1)
top1_event  = event_probs.argmax(dim=-1).item()

Training Data

Trained on MIMIC-IV v3.1 (PhysioNet credentialed access required):

• 100k patient sequences, male cohort
• 48 event categories derived from ICD-10 diagnosis and procedure codes
• External validation: 1,120 BWH patients (JSD=0.27 domain shift)

Intended Use & Limitations

Intended use: Research on clinical event prediction, EHR sequence modelling, benchmarking.

Not intended for: Direct clinical decision-making without prospective validation.

Limitations:

• Trained on MIMIC-IV (US academic medical center); performance under distribution shift varies
• Structured features (labs, vitals, medications) required at inference; performance degrades when unavailable
• 48-class coarse event vocabulary; within-cluster distinctions are discarded
• Calibration analysis pending

Citation

@article{rouhollahi2026cadence,
  title   = {Cadence: A Benchmark Evaluation of the Narrative Velocity Framework for Next Clinical Event Prediction in {MIMIC-IV}},
  author  = {Rouhollahi, Amir and Nezami, Farhad R.},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.05.06.722409},
  url     = {https://doi.org/10.64898/2026.05.06.722409}
}

License

MIT License. The pretrained checkpoint is provided for research use only. MIMIC-IV data is subject to the PhysioNet Credentialed Health Data License.