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keystats/Kenya-Clinical-Reasoning-Challenge

Domain:

healthcarenatural language processing

Record type:

model
Creator:
key
Host:
AI model for the Kenya Clinical Reasoning Challenge on Zindi, predicting clinician-like responses to medical prompts using NLP. # 🌍 Kenya Clinical Reasoning Challenge 🩺 --- ## πŸ“œ Overview This project was developed for the **Kenya Clinical Reasoning Challenge** hosted on Zindi. The goal was to **predict clinician responses** to medical prompts β€” pushing the boundaries of AI-assisted healthcare reasoning. The project aimed to surpass baseline LLMs such as **GPT-4.0, LLAMA, and GEMINI** using a **ROUGE score** evaluation. --- ## 🎯 Objective > Build a text-to-text generation model capable of reasoning like a clinician β€” and aim for **0.50+** ROUGE score. --- ## πŸ› οΈ Approach ### **Step 1 β€” Data Exploration** πŸ” Investigated dataset structure, prompts, and clinician answers. ### **Step 2 β€” Preprocessing** 🧹 Cleaned and tokenized text, handled missing values, created paired training data. ### **Step 3 β€” Model Selection** βš™οΈ Chose a **Transformer-based model** optimized for clinical reasoning tasks. ### **Step 4 β€” Training** πŸ’» Trained on GPU with tuned hyperparameters to prevent overfitting. ### **Step 5 β€” Evaluation** πŸ“Š Measured **ROUGE scores** on public and private leaderboards. ### **Step 6 β€” Submission** πŸ“‚ Prepared predictions in Zindi’s required format. --- ## πŸ“ˆ Results πŸ† **Public Score:** `0.395355497` πŸ† **Private Score:** `0.414785791` πŸ₯‡ **Leaderboard Position:** **Top 45** These results positioned the solution among the best-performing entries. --- ## ⚠️ Weaknesses - πŸ“‰ Limited training data reduced adaptability to rare reasoning cases. - πŸ€” Some answers lacked **context-specific depth**. - πŸ“ Long prompts sometimes produced **truncated responses**. --- ## πŸš€ Future Improvements To achieve **0.5+**, here’s what’s next: 1. πŸ“š Add **medical domain knowledge bases**. 2. πŸ€– Fine-tune **larger transformer architectures**. 3. 🎯 Use **prompt-engineering** for better context retention. 4. πŸ“Š Data augmentation for underrepresented categories. 5. 🧩 Ensemble multiple architectures for robustness. --- ## πŸ‘¨β€πŸ’» Author **Jackson Kahungu** πŸ“… _Project Year: 2025_ --- ⚠ **Disclaimer**: For …