README.md

---
language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - emotion-classification
  - healthcare
  - distilbert
  - patient-doctor-conversations
  - text-classification
  - clinical-AI
  - mental-health
model_index:
  - name: patient-emotion-classifier
    results:
      - task:
          type: text-classification
        metrics:
          - type: accuracy
            value: 0.713
          - type: f1
            value: 0.722
---

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# 🤖 Patient Emotion Classifier

**Advanced AI-Powered Emotion Recognition for Healthcare Dialogues**

*Part of the Blended AI+X Initiative — Bridging Artificial Intelligence and Healthcare*

---

[![Model](https://img.shields.io/badge/Model-DistilBERT-blue)](https://huggingface.co/distilbert/distilbert-base-uncased)
[![License](https://img.shields.io/badge/License-Apache--2.0-green)](LICENSE)
[![Performance](https://img.shields.io/badge/F1--Score-72.2%25-orange)]()

</div>

## 🔬 Overview

We are thrilled to introduce **Patient Emotion Classifier**, a state-of-the-art NLP model engineered to understand emotional nuances in patient-doctor conversations. 

This model represents our commitment to advancing **AI for Healthcare (AI+X)**, leveraging cutting-edge transformer architectures to bridge the gap between artificial intelligence and compassionate care.

### Key Capabilities

- **Multiclass Emotion Recognition** — Identifies 6 distinct emotional states in clinical dialogues
- **Healthcare-Optimized** — Specifically trained on medical conversation data
- **Production-Ready** — Deployable via REST API for real-time inference
- **Lightweight & Efficient** — Built on DistilBERT for fast inference

## 🎯 Emotion Categories

Our model classifies emotional states into **6 clinically-relevant categories**:

| Category | Description |
|----------|-------------|
| 😐 **Neutral** | Objective, non-emotional statements |
| 😰 **Anxiety/Fear** | Patient expresses worry, concern, or fear |
| 😠 **Anger/Frustration** | Patient shows frustration or displeasure |
| 😢 **Sadness/Helplessness** | Patient feels down or hopeless |
| 🤔 **Confusion/Doubt** | Patient expresses uncertainty or questions |
| 🙏 **Gratitude/Relief** | Patient conveys thanks or relief |

## 📊 Model Performance

### Overall Metrics

| Metric | Value |
|--------|-------|
| **Accuracy** | **71.3%** |
| **Macro F1** | **0.722** |
| Weighted F1 | 0.72 |

### Per-Class Performance

| Emotion | Precision | Recall | F1-Score |
|---------|-----------|--------|----------|
| Neutral | 0.75 | 0.78 | 0.76 |
| Anxiety/Fear | 0.52 | 0.63 | 0.57 |
| Anger/Frustration | 0.80 | 0.73 | 0.76 |
| Sadness/Helplessness | 0.65 | 0.55 | 0.60 |
| Confusion/Doubt | 0.60 | 0.58 | 0.59 |
| Gratitude/Relief | 0.72 | 0.75 | 0.73 |

### Label Distribution

![Label Distribution](data/label_distribution.png)

## 🚀 Quick Start

### 1. Install Dependencies

```bash
pip install -r requirements.txt

### 2. Launch the Service

```bash
cd see
python app.py

### 3. Access the Interface

http://localhost:8002

## 📚 Dataset

This model was trained on a meticulously curated subset of medical dialogues:

- **Original Source**: [Chinese MedDialog Dataset](https://tianchi.aliyun.com/dataset/92110) — Alibaba Cloud Tianchi
- **Post-Processing**: Carefully filtered, translated, and annotated for emotion classification
- **Total Samples**: 28,280 annotated dialogues
- **Categories**: 6 emotion labels
- **Language**: English

## 📚 References

1. **MedDialog Dataset**  
   Chinese Medical Dialogue Dataset. Alibaba Cloud Tianchi.  
   https://tianchi.aliyun.com/dataset/92110

2. **DistilBERT**  
   Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). *DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter*. arXiv preprint arXiv:1910.01108.  
   https://arxiv.org/abs/1910.01108

## 🏗️ Technical Specifications

| Component | Details |
|-----------|---------|
| **Base Architecture** | [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) |
| **Task Type** | 6-class emotion classification |
| **Max Sequence Length** | 512 tokens |
| **Framework** | PyTorch + Transformers |

## 📁 Project Structure

patient-emotion-analysis/
├── best_model/          # Fine-tuned model weights
├── see/                 # Inference service
│   ├── app.py          # Web application
│   ├── inference.py    # Core inference logic
│   └── templates/      # UI templates
├── data/               # Training & evaluation data
├── requirements.txt    # Dependencies
└── README.md           # This file

---

<div align="center">

**Blended AI+X Initiative** — *Advancing Healthcare Through Intelligence*

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