nih-adrd-pubmedbert

NIH AD/ADRD Classifier (PubMedBERT)

A binary text classifier that predicts whether a research project's title + abstract belongs to NIH's Alzheimer's Disease / Alzheimer's Disease-Related Dementias (AD/ADRD) research portfolio. Fine-tuned from PubMedBERT / BiomedBERT on NIH's own portfolio labels — not a general "neurodegenerative disease" classifier.

Model description

• Base model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext (BERT-base, domain vocabulary pretrained from scratch on PubMed abstracts + PMC full text)
• Task: binary sequence classification, input = (title, abstract) as a tokenizer pair (proper token_type_ids segmentation, not a manually concatenated string)
• Max sequence length: 512 tokens
• Output: softmax probability of the positive (AD/ADRD) class

Training data

Source: NIH ExPORTER bulk data (Projects + Abstracts, joined on APPLICATION_ID), fiscal years 2018–2024.

Label: positive iff the award's NIH_SPENDING_CATS field contains one of these three exact RCDC category strings (an explicit allowlist, not a substring heuristic):

• Alzheimer's Disease (RCDC code 40)
• Alzheimer's Disease Related Dementias (ADRD) (code 3254)
• Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) (code 3246)

This is NIH's own strict scope — it does not include Parkinson's disease, ALS, or Huntington's disease, which NIH tracks under their own separate RCDC categories.

Preprocessing:

• Exact-duplicate (title, abstract) pairs removed (renewal/resubmission text reused verbatim across fiscal years — ~50% of the raw corpus)
• Split grouped by CORE_PROJECT_NUM (stratified, 70/15/15) so that no NIH project's multi-year renewals cross the train/val/test boundary
• Train-set negatives: guaranteed inclusion of two targeted hard-negative cohorts (NIH awards coded Parkinson's/ALS/Huntington's/VCID but not AD/ADRD; awards mentioning amyloid-family vocabulary but not AD/ADRD-labeled), plus random fill to a 10:1 negative:positive ratio. Validation and test sets keep the natural class distribution.

Training procedure

• Loss: class-weighted cross-entropy (weight = train-set negative:positive ratio)
• Optimizer: AdamW, lr 2e-5, weight decay 0.01, warmup ratio 0.1
• 3 epochs, batch size 16 (train) / 32 (eval), fp16
• Model selection: best checkpoint by validation PR-AUC (positive rate ≈ 6-9%, so PR-AUC rather than accuracy)
• Hardware: single A10G GPU (~70 minutes)

Evaluation

Held-out test set (natural class distribution, never used for training or model selection):

A near-duplicate leakage audit (TF-IDF cosine similarity between every test row and its nearest training neighbor) found PR-AUC stable (0.960 → 0.959) after excluding the most similar 1-2% of test rows, so this is not inflated by train/test text overlap.

On threshold choice: the raw softmax output is not a calibrated posterior probability — training uses negative downsampling plus inverse-frequency class weighting, which shifts the effective training prevalence well above NIH's true ~6% base rate. Use PR-AUC/ROC-AUC for ranking, and pick an operating threshold from a validation set matching your deployment distribution rather than assuming 0.5. Platt scaling (fit on the logit, not the raw probability) is a reasonable post-hoc calibration if you need an actual probability estimate.

Intended use and limitations

• Trained and evaluated on NIH-style grant abstracts. Applied informally to EU/Horizon (CORDIS) project text as a cross-domain check; agreement with an independent LLM-based classification (Claude + GPT, matching disease scope) was substantial-to-almost-perfect (Cohen's κ ≈ 0.82), but this is not a validated cross-domain benchmark — treat out-of-domain predictions as a signal to review, not a final decision.
• Known residual failure mode: occasional false positives on text containing surface-level token overlap with AD-associated vocabulary in an unrelated context (e.g., generic AI/technology-infrastructure projects with broad "health" framing). A prior version of this model also collided on abbreviation overlap (e.g. "AD" as Anno Domini in historical text); this is substantially reduced but not eliminated by the hard-negative training here.
• Designed as a high-recall prefilter / triage signal, not a sole final-decision system — best used alongside expert or LLM review of flagged candidates, particularly for anything outside the strict NIH AD/ADRD definition it was trained on.

How to use

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("rucolaes/nih-adrd-pubmedbert")
model = AutoModelForSequenceClassification.from_pretrained("rucolaes/nih-adrd-pubmedbert").eval()

title = "..."
abstract = "..."
inputs = tokenizer(title, abstract, truncation=True, max_length=512, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
prob_adrd = torch.softmax(logits, dim=1)[0, 1].item()