Summary of Interpretable Neural Temporal Point Processes For Modelling Electronic Health Records, by Bingqing Liu
Interpretable Neural Temporal Point Processes for Modelling Electronic Health Records
by Bingqing Liu
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed inf2vec framework is an interpretable approach to modeling electronic health records (EHRs) as temporal sequences of medical visits. Building upon neural temporal point process (NTPP) and drawing inspiration from word2vec and Hawkes processes, inf2vec directly parameterizes event influences and can be learned end-to-end. The model demonstrates superiority in both event prediction and learning type-type influences. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand the relationships between different medical visits recorded in electronic health records. This approach uses special kinds of artificial intelligence called neural networks to figure out how these visits are connected. It’s like creating a map of how one visit might lead to another, and why some visits might be more important than others. The researchers show that this new method works better than other approaches at predicting what will happen next in a patient’s medical history. |
Keywords
* Artificial intelligence * Word2vec