Summary of Leave No Patient Behind: Enhancing Medication Recommendation For Rare Disease Patients, by Zihao Zhao et al.
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
by Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel model called Robust and Accurate REcommendations for Medication (RAREMed) to provide tailored and effective drug combinations for patients with diverse clinical information. The existing approaches suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. RAREMed leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases, employing a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. The model also introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help doctors choose the right medicines for their patients. Right now, some medicine-recommending systems are not fair because they work better for people with common illnesses than those with rare ones. The researchers created a new model called RAREMed that can give good recommendations for both types of patients. They used a special kind of learning to make it more accurate and fair. The model is very good at looking at all the different pieces of information about a patient, like what diseases they have or what procedures they’ve had done. By using this new model, doctors might be able to give their patients better treatments. |
Keywords
» Artificial intelligence » Encoder » Self supervised » Transformer
