Summary of Mplite: Multi-aspect Pretraining For Mining Clinical Health Records, by Eric Yang et al.
MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records
by Eric Yang, Pengfei Hu, Xiaoxue Han, Yue Ning
First submitted to arxiv on: 17 Nov 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 proposed novel framework, MPLite, utilizes Multi-aspect Pretraining with Lab results through a light-weight neural network to enhance medical concept representation and predict future health outcomes of individuals. By incorporating both structured medical data and additional information from lab results, our approach fully leverages patient admission records. The pretraining module predicts medical codes based on lab results, ensuring robust prediction by fusing multiple aspects of features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to use electronic health records (EHRs) to predict what might happen to patients in the future. Doctors and hospitals are collecting lots of digital information about patients’ health, which can be used to train machines to make predictions. The problem is that most machine learning models only look at one visit or record at a time, but doctors need to know how a patient’s health will change over time. This paper proposes a new way to use lab test results and other medical information to help predict what might happen in the future. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Pretraining
