StringJammer/patient-emotion-classifier

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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

  ![Performance]()

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🔬 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:

📊 Model Performance

Overall Metrics

Per-Class Performance

Label Distribution

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🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

### 2. Launch the Service

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

1. 1.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

📁 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

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Blended AI+X Initiative — Advancing Healthcare Through Intelligence

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