Summary of Unveiling Latent Causal Rules: a Temporal Point Process Approach For Abnormal Event Explanation, by Yiling Kuang et al.
Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
by Yiling Kuang, Chao Yang, Yang Yang, Shuang Li
First submitted to arxiv on: 3 Feb 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 This paper proposes an automated method for uncovering causal relationships between events, such as sudden changes in patient’s health, to facilitate quick diagnoses and precise treatment planning. The approach employs temporal point processes to model the events of interest, and uses an Expectation-Maximization algorithm to discover latent rules explaining the occurrence of events. The E-step calculates the likelihood of each event being explained by each discovered rule, while the M-step updates both the rule set and model parameters to enhance the likelihood function’s lower bound. Notably, the approach optimizes the rule set in a differential manner. The paper showcases accurate performance using synthetic and real healthcare datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand why unexpected things happen in hospitals, like when a patient suddenly gets worse. To do this, it creates rules that say “if something happens, then something else is likely to happen.” This can help doctors quickly figure out what’s wrong and give the right treatment. The researchers use special math tools to find these rules, and test them on fake and real hospital data. Their results show that their method works well and can help us make better decisions in hospitals. |
Keywords
* Artificial intelligence * Likelihood
