Model: monajm36/ohca-classifier-v8
Author: monajm36
Pipeline: text-classification
Library: transformers
Tags: transformers, pytorch, clinical, healthcare, emergency-medicine, OHCA, PubMedBERT, MIMIC, patient-level-split, text-classification, en, base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract, base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract, license:mit, model-index, endpoints_compatible, region:us
Base models (finetune): id: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
Card metadata: license: mit
language: en
library_name: transformers
pipeline_tag: text-classification
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract
tags: clinical, healthcare, emergency-medicine, OHCA, PubMedBERT, MIMIC, patient-level-split
model-index: name: OHCA Classifier v8 (PubMedBERT fine-tuned)
results: task: type: text-classification
name: Binary OHCA detection (OHCA vs non-OHCA)
dataset: name: Internal (MIMIC-derived discharge notes)
type: text
split: test (patient-level)
metrics: type: recall
name: Sensitivity (Recall)
value: 1
verified: no, type: specificity
name: Specificity
value: 0.879
verified: no, type: precision
name: PPV (Precision)
value: 0.562
verified: no, type: npv
name: NPV
value: 1
verified: no, type: f1
name: F1-score
value: 0.72
verified: no, type: auc
name: ROC-AUC
value: 0.971
verified: no
Likes: 0 | All-time downloads: 0
Created: 2025-11-12 02:07:34