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keystats/Kenya-Clinical-Reasoning-Challenge

Domaine:

healthcarenatural language processing

Type de record:

model
Créateur:
key
Hôte:
AI model for the Kenya Clinical Reasoning Challenge on Zindi, predicting clinician-like responses to medical prompts using NLP. # 🌍 Kenya Clinical Reasoning Challenge 🩺 --- ## 📜 Overview This project was developed for the **Kenya Clinical Reasoning Challenge** hosted on Zindi. The goal was to **predict clinician responses** to medical prompts — 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 — and aim for **0.50+** ROUGE score. --- ## 🛠️ Approach ### **Step 1 — Data Exploration** 🔍 Investigated dataset structure, prompts, and clinician answers. ### **Step 2 — Preprocessing** 🧹 Cleaned and tokenized text, handled missing values, created paired training data. ### **Step 3 — Model Selection** ⚙️ Chose a **Transformer-based model** optimized for clinical reasoning tasks. ### **Step 4 — Training** 💻 Trained on GPU with tuned hyperparameters to prevent overfitting. ### **Step 5 — Evaluation** 📊 Measured **ROUGE scores** on public and private leaderboards. ### **Step 6 — Submission** 📂 Prepared predictions in Zindi’s required format. --- ## 📈 Results 🏆 **Public Score:** `0.395355497` 🏆 **Private Score:** `0.414785791` 🥇 **Leaderboard Position:** **Top 45** These results positioned the solution among the best-performing entries. --- ## ⚠️ Weaknesses - 📉 Limited training data reduced adaptability to rare reasoning cases. - 🤔 Some answers lacked **context-specific depth**. - 📝 Long prompts sometimes produced **truncated responses**. --- ## 🚀 Future Improvements To achieve **0.5+**, here’s what’s next: 1. 📚 Add **medical domain knowledge bases**. 2. 🤖 Fine-tune **larger transformer architectures**. 3. 🎯 Use **prompt-engineering** for better context retention. 4. 📊 Data augmentation for underrepresented categories. 5. 🧩 Ensemble multiple architectures for robustness. --- ## 👨‍💻 Author **Jackson Kahungu** 📅 _Project Year: 2025_ --- ⚠ **Disclaimer**: For …