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March 5, 2024Journal of Infection16 citationsOpen Access

Clinical features-based machine learning models to separate sexually transmitted infections from other skin diagnoses

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NSNyi Nyi SoePLPhyu Mon LattZYZhen Yu

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Abstract

Many sexual health services are overwhelmed and cannot cater for all the individuals who present with sexually transmitted infections (STIs). Digital health software that separates STIs from non-STIs could improve the efficiency of clinical services. We developed and evaluated a machine learning model that predicts whether patients have an STI based on their clinical features.

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Cite This Study

Soe et al. (2024) studied this question.

synapsesocial.com/papers/68e758bcb6db6435876d0850https://doi.org/10.1016/j.jinf.2024.106128
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata2024 · 26 citations
  2. 2Accuracy of Symptom Checker for the Diagnosis of Sexually Transmitted Infections using Machine Learning and Bayesian Network Algorithms2024
  3. 3Beyond the Clinic: Artificial Intelligence Transforming STI Self-Assessment, Early Detection, and Personalized Decision Support2026
  4. 4Early detection of sexually transmitted infections from skin lesions with deep learning: a systematic review and meta-analysis2025
  5. 5Evaluating the Performance of Mobile Machine-Learning Platforms for Syphilis Symptom Screening2026