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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

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

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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 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