Summary of Contrastive Learning on Medical Intents For Sequential Prescription Recommendation, by Arya Hadizadeh Moghaddam et al.
Contrastive Learning on Medical Intents for Sequential Prescription Recommendation
by Arya Hadizadeh Moghaddam, Mohsen Nayebi Kerdabadi, Mei Liu, Zijun Yao
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Attentive Recommendation with Contrasted Intents (ARCI), a multi-level transformer-based method designed to capture diverse temporal relationships in Electronic Health Records (EHRs) across consecutive visits. The goal is to develop a sophisticated sequential model that disentangles complex relationships and establishes multiple health profiles for the same patient, enabling comprehensive drug recommendation considering different medical intents. ARCI uses contrastive learning to link specialized medical intents to transformer heads, extracting distinct temporal paths associated with different health profiles. Experimental results on two real-world datasets demonstrate superior performance compared to state-of-the-art methods, providing interpretable insights for healthcare practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help doctors recommend the best medicine for patients by looking at patterns in electronic health records. It’s like trying to understand what someone’s doctor would have written if they visited multiple times, and figuring out how different medical issues are connected. The researchers created a new model called ARCI that can find these connections and use them to make better recommendations. They tested it on real patient data and found it worked better than other methods. |
Keywords
» Artificial intelligence » Transformer
