SDOHv7 Open-Source Medical Text Classification Model - Automatically Train and Identify Social Determinants of Health Content

Sdohv7

Developed by ClinicalNLP

This is an auto-trained model for text classification in the healthcare domain, specifically focused on identifying content related to Social Determinants of Health (SDOH).

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #Medical Text Classification #Social Determinants of Health Identification #High-Precision Classification

Downloads 35

Release Time : 2/24/2023

Model Overview

Developed through an automated training process, this model is specifically designed to classify and identify statements related to Social Determinants of Health in medical texts, such as housing instability, food insecurity, financial hardship, and other social influencing factors.

Model Features

Social Determinants of Health Identification

Specialized in classifying and identifying social determinants in healthcare contexts

High Accuracy

Validation metrics show both accuracy and F1 score reaching 0.99

Auto-Training Optimization

Utilizes AutoTrain process for training and optimization

Low Carbon Emissions

Training process emitted only 0.0113 grams of CO2

Model Capabilities

Medical Text Classification

Social Determinants Identification

Multi-class Text Classification

Use Cases

Medical Record Analysis

Patient Social Risk Assessment

Identifying social risk factors from electronic health records

Accurately identifies social risk factors like housing instability and food insecurity

Public Health Research

Analyzing distribution of social determinants in large-scale medical text data

Provides data support for public health policies

Clinical Decision Support

Social Service Referral

Identifying patients needing social service support

Helps medical teams promptly refer patients to appropriate social service agencies

🚀 Model Trained Using AutoTrain

This model is designed for multi - class text classification in the healthcare domain, specifically focusing on social determinants of health (SDOH). It offers high - accuracy predictions and can be easily integrated into various applications.

🚀 Quick Start

You can start using this model right away. Below are two common ways to access it.

✨ Features

Multi - class Classification: Capable of classifying text into multiple classes, which is very useful in the healthcare field, especially for analyzing social determinants of health.
High Accuracy: With an accuracy of 0.990, it provides reliable classification results.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/reachosen/autotrain-sdohv7-3701198597

Advanced Usage

Or use the Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("reachosen/autotrain-sdohv7-3701198597", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("reachosen/autotrain-sdohv7-3701198597", use_auth_token=True)

inputs = tokenizer("The Patient is homeless", return_tensors="pt")

outputs = model(**inputs)

📚 Documentation

Validation Metrics

Property	Details
Loss	0.057
Accuracy	0.990
Macro F1	0.990
Micro F1	0.990
Weighted F1	0.990
Macro Precision	0.990
Micro Precision	0.990
Weighted Precision	0.991
Macro Recall	0.990
Micro Recall	0.990
Weighted Recall	0.990

Model Information

Property	Details
Problem type	Multi - class Classification
Model ID	3701198597
CO2 Emissions (in grams)	0.0113
Model Type	Text Classification
Training Data	reachosen/autotrain-data-sdohv7

📄 License

This model is licensed under the Apache 2.0 license.

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