AI model for the Kenya Clinical Reasoning Challenge on Zindi, predicting clinician-like responses to medical prompts using NLP.
# π Kenya Clinical Reasoning Challenge π©Ί
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## π 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.
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## π― Objective
> Build a text-to-text generation model capable of reasoning like a clinician β and aim for **0.50+** ROUGE score.
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## π οΈ Approach
### **Step 1 β Data Exploration**
π Investigated dataset structure, prompts, and clinician answers.
### **Step 2 β Preprocessing**
π§Ή Cleaned and tokenized text, handled missing values, created paired training data.
### **Step 3 β Model Selection**
βοΈ Chose a **Transformer-based model** optimized for clinical reasoning tasks.
### **Step 4 β Training**
π» Trained on GPU with tuned hyperparameters to prevent overfitting.
### **Step 5 β Evaluation**
π Measured **ROUGE scores** on public and private leaderboards.
### **Step 6 β Submission**
π Prepared predictions in Zindiβs required format.
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## π Results
π **Public Score:** `0.395355497`
π **Private Score:** `0.414785791`
π₯ **Leaderboard Position:** **Top 45**
These results positioned the solution among the best-performing entries.
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## β οΈ Weaknesses
- π Limited training data reduced adaptability to rare reasoning cases.
- π€ Some answers lacked **context-specific depth**.
- π Long prompts sometimes produced **truncated responses**.
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## π Future Improvements
To achieve **0.5+**, hereβs whatβs next:
1. π Add **medical domain knowledge bases**.
2. π€ Fine-tune **larger transformer architectures**.
3. π― Use **prompt-engineering** for better context retention.
4. π Data augmentation for underrepresented categories.
5. π§© Ensemble multiple architectures for robustness.
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## π¨βπ» Author
**Jackson Kahungu**
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_Project Year: 2025_
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β **Disclaimer**: For β¦