Alphadavethedon/kenya-clinical-reasoning-challenge

This project focuses on developing machine learning models to replicate clinical reasoning in Kenyan healthcare settings. The challenge involves predicting clinicians' responses to complex medical vignettes, simulating real-world decision-making in rural and resource-constrained environments. **Clinical Reasoning with T5 Transformer** **Zindi Competition: Kenya Clinical Reasoning Challenge** *Notebook Link:* Open in Colab --- ## **📌 Overview** This notebook implements a **fine-tuned T5 Transformer model** for clinical text classification, submitted to the Kenya Clinical Reasoning Challenge on Zindi. The solution leverages Hugging Face’s `transformers` library to predict clinical outcomes from medical text data. **🔗 Share this notebook:** --- **Certificate Verification**: Validate competition participation and ranking via the official Zindi certificate. --- ## **🚀 Key Features** 1. **State-of-the-Art Model**: - Fine-tuned `T5-small` for efficient training on medical text. - Optimized for Colab’s free-tier GPU (FP16, gradient accumulation). 2. **End-to-End Pipeline**: - Data loading → Preprocessing → Training → Submission. - Includes Zindi submission validation (`validate_submission()`). 3. **Competition-Ready**: - Logs metrics (accuracy, F1-score). - Saves predictions in Zindi’s required CSV format. --- ## **🛠️ Technical Setup** ### **Dependencies** ```bash pip install transformers datasets evaluate accelerate pandas numpy ``` ### **Model Architecture** ```python from transformers import T5ForConditionalGeneration, TrainingArguments model = T5ForConditionalGeneration.from_pretrained("t5-small") training_args = TrainingArguments( output_dir="./results", per_device_train_batch_size=4, fp16=True, # GPU acceleration num_train_epochs=3 ) ``` --- ## **📊 Data Preprocessing** - **Input Format**: Clinical text (e.g., `"Patient with fever and cough"`). - **Tokenization**: ```python tokenizer = T5Tokenizer.from_pretrained("t5-small") inputs = tokenizer("clinical text: " + text, truncation=True, padding="max_length") ``` --- ## **⚙️ Training** **Hyperparameters**: | Parameter | Value | |--------------------|-----------| | Learning Rate | `3e-5` | | Batch Size | `4` | | Epochs | `3` | | FP16 …