ashishkgpian
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biobert_icd9_classifier_ehr

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- biobert
+- medical-nlp
+- icd-9
+- classification
+- healthcare
+license: apache-2.0
+language:
+- en
+base_model:
+- dmis-lab/biobert-v1.1
+pipeline_tag: text-classification
 ---
+# Model Card for BioBERT Fine-tuned on MIMIC-3 for ICD-9 Code Classification
 ## Model Details
 ### Model Description
+This is a BioBERT model fine-tuned on the MIMIC-3 (Medical Information Mart for Intensive Care) corpus specifically for ICD-9 code classification. The model is designed to predict medical diagnostic codes based on Electronic Health Record (EHR) and symptom text inputs.
+- **Developed by:** [Researcher/Institution Name - to be added]
+- **Model type:** Transformer-based medical language model (BioBERT)
+- **Language(s):** English (Medical Domain)
+- **License:** [License to be specified]
+- **Finetuned from model:** BioBERT base model
+### Model Sources
+- **Repository:** [GitHub/Model Repository Link - to be added]
+- **Paper:** [Research Paper Link - to be added]
 ## Uses
 ### Direct Use
+The primary use of this model is to automatically classify medical conditions by predicting relevant ICD-9 diagnostic codes from clinical text, such as electronic health records, medical notes, or symptom descriptions.
+### Downstream Use
+This model can be integrated into:
+- Clinical decision support systems
+- Medical coding automation
+- Electronic health record (EHR) analysis tools
+- Healthcare informatics research
 ### Out-of-Scope Use
+- The model should not be used for direct medical diagnosis without professional medical oversight
+- It is not intended to replace clinical judgment
+- Performance may vary with text outside the medical domain or significantly different from the training corpus
 ## Bias, Risks, and Limitations
+- The model's performance is limited to the medical conditions and coding patterns in the MIMIC-3 dataset
+- Potential biases from the original training data may be present
+- Accuracy can be affected by variations in medical terminology, writing styles, and complex medical cases
 ### Recommendations
+- Validate model predictions with medical professionals
+- Use as a supportive tool, not a replacement for expert medical assessment
+- Regularly evaluate performance on new datasets
+- Be aware of potential demographic or contextual biases in the predictions
 ## How to Get Started with the Model
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained('model_path')
+tokenizer = AutoTokenizer.from_pretrained('model_path')
+# Example prediction function (similar to the provided get_predictions function)
+def predict_icd9_codes(input_text, threshold=0.8):
+    # Tokenize input
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512, padding='max_length')
+    # Get model predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.sigmoid(outputs.logits)
+    # Filter predictions above threshold
+    predicted_codes = [model.config.id2label[i] for i in (predictions > threshold).nonzero()[:, 1]]
+    return predicted_codes
+```
 ## Training Details
 ### Training Data
+- **Dataset:** MIMIC-3 Corpus
+- **Domain:** Medical/Clinical text
+- **Content:** Electronic Health Records (EHR)
 ### Training Procedure
+#### Preprocessing
+- Text tokenization
+- Maximum sequence length: 512 tokens
+- Padding to uniform length
+- Potential text normalization techniques
 #### Training Hyperparameters
+- **Base Model:** BioBERT
+- **Training Regime:** Fine-tuning
+- **Precision:** [Specify training precision, e.g., mixed precision]
 ## Evaluation
 ### Testing Data, Factors & Metrics
 #### Testing Data
+- Held-out subset of MIMIC-3 corpus
+- Diverse medical cases and documentation styles
 #### Metrics
+- Precision
+- Recall
+- F1-Score
+- Multi-label classification metrics
 ## Environmental Impact
+- Estimated carbon emissions to be calculated
+- Compute details to be specified
+## Technical Specifications
+### Model Architecture
+- **Base Model:** BioBERT
+- **Task:** Multi-label ICD-9 Code Classification
+## Citation
+[Citation information to be added when research is published]
+## More Information
+For more details about the model's development, performance, and usage, please contact the model developers.