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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Related Experiment Video

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Design and Analysis for Fall Detection System Simplification
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A machine learning based framework for identifying consumer product injuries from social media data.

Harmya Bhatt1, Souvik Das2, Yi Jade Han3

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Injury
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework to rapidly detect product-related injuries using social media data, improving consumer safety surveillance and enabling faster interventions.

Keywords:
Injury surveillanceNatural language processingProduct safetySocial media analysisText mining

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Area of Science:

  • Consumer Product Safety
  • Public Health Surveillance
  • Machine Learning Applications

Background:

  • Millions of product-related injuries occur annually, with traditional surveillance methods causing delays in identifying injury patterns.
  • Current methods, relying on hospital data, are slow, hindering timely preventive actions like product recalls.
  • This delay leads to continued consumer product-related injuries.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) framework for real-time injury surveillance.
  • To extract product injury details from social media for rapid trend identification and intervention.
  • To improve the speed and efficacy of consumer product safety monitoring.

Main Methods:

  • A two-stage ML framework was proposed, utilizing social media posts (Reddit) and National Electronic Injury Surveillance System (NEISS) data.
  • Stage 1 classified posts as injury-related or not using ML models trained on diverse datasets.
  • Stage 2 predicted body parts injured and injury diagnosis codes from injury-related posts using ML models.

Main Results:

  • Stage 1 models (LSTM, GRU) achieved an F1-score of 72% for classifying injury-related posts.
  • Stage 2 models (SGD) demonstrated an F1-score of 86% for predicting body parts injured.
  • Stage 2 models also achieved a 76% F1-score for injury diagnosis code prediction.

Conclusions:

  • The ML framework shows promising accuracy for injury surveillance.
  • Social media data analysis via this framework can identify emerging product-related injury trends.
  • The proposed system can significantly enhance existing public health surveillance efforts for consumer products.