vitals-interpreter-model

Vitals Interpreter Model (Fine-Tuned LLM)

Project Overview

This project implements a fine-tuned transformer model that interprets basic human vital signs and generates structured health guidance.

The model takes numerical vitals as input and produces a concise, human-readable output consisting of:

• Health status classification
• Suggested action/advice

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Objective

To build a lightweight, efficient AI system that:

• Understands structured vital inputs
• Classifies health condition into categories
• Generates consistent and controlled responses

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Model Details

• Base Model: t5-small
• Architecture: Encoder-Decoder Transformer
• Fine-Tuning Type: Supervised Fine-Tuning (SFT)
• Framework: Hugging Face Transformers

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Input Format

interpret vitals -> heart rate X, blood pressure Y/Z, temperature T

Example:

interpret vitals -> heart rate 125, blood pressure 150/95, temperature 100

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Output Format

Status: <Normal | High | Low | Critical> | Advice: <short guidance>

Example Output:

Status: High | Advice: Monitor and consult doctor

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Dataset

• Type: Synthetic dataset
• Size: ~30–50 samples
• Design Approach:
  • Based on medically accepted ranges of vital signs
  • Balanced across categories:
    • Normal
    • High
    • Low
    • Critical

Why Synthetic Data?

Due to lack of publicly available labeled text datasets for this task, a controlled dataset was generated to:

• Ensure consistency in output format
• Improve learning efficiency
• Avoid noisy or unstructured data

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Training Configuration

• Epochs: 20–30
• Batch Size: 2–4
• Learning Rate: 5e-5
• Max Sequence Length: 64
• Tokenizer: AutoTokenizer (T5)

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Evaluation

Method:

• Manual testing with unseen inputs
• Verification of:
  • Correct classification (Normal / High / Low / Critical)
  • Proper output structure
  • Relevance of advice

Sample Predictions:

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How to Use

Installation

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

Author:
Archee Sinha
B.Tech CSE (AI)
ABES Institute of Technology