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Methods of reducing fever

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The signs and symptoms of fever include hot and dry skin, flushed face, thirst, muscle aches, anorexia, headache, tachycardia, tachypnea, and fatigue. Elevated body temperature is reduced using two methods: pharmacological and nonpharmacological. Proper identification and treatment of the root cause of a fever is of utmost importance.
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Related Experiment Video

Updated: Jun 4, 2025

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Using transformer-based models and social media posts for heat stroke detection.

Sumiko Anno¹, Yoshitsugu Kimura², Satoru Sugita³

¹Graduate School of Global Environmental Studies, Sophia University, Tokyo, Japan. sumiko_anno@sophia.ac.jp.

Scientific Reports

|January 3, 2025

Summary

This study shows that artificial intelligence can accurately identify real heat stroke tweets from social media. Combining this with public health data helps detect heat risks early.

Keywords:

Bidirectional encoder representations from transformers Event-based surveillance Heat stroke Language understanding with knowledge-based embeddings Tweets

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Area of Science:

Public Health Surveillance
Artificial Intelligence in Healthcare
Climate Change Impacts

Background:

Event-based surveillance is vital for public health.
Social networking services (SNS) show potential for early health crisis detection.
Reliability of SNS data for health surveillance remains a challenge due to subjectivity.

Purpose of the Study:

To assess transformer-based language models for classifying Japanese heat stroke tweets.
To evaluate the combination of SNS data and AI for event-based public health surveillance.
To visualize spatiotemporal correlations between classified tweets and heat stroke emergencies.

Main Methods:

Utilized transformer-based pretrained language models to classify Japanese tweets related to heat stroke.
Assessed classification accuracy of tweets as true or false.
Visualized spatiotemporal data of classified tweets and heat stroke emergency medical evacuees.

Main Results:

Transformer-based models demonstrated good performance in classifying heat stroke-related tweets.
Spatiotemporal and animated video visualizations showed a reasonable correlation between data sources.
The study confirmed the potential of AI and SNS data for high spatiotemporal public health surveillance.

Conclusions:

Japanese tweets, analyzed with deep learning and transformer networks, show promise for event-based surveillance.
This approach enables early detection of heat stroke risks at high spatiotemporal resolutions.
Combining AI with SNS data offers a viable strategy for enhanced public health monitoring.