Summary of A Review on Generative Ai Models For Synthetic Medical Text, Time Series, and Longitudinal Data, by Mohammad Loni et al.
A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data
by Mohammad Loni, Fatemeh Poursalim, Mehdi Asadi, Arash Gharehbaghi
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A novel scoping review on practical models for generating synthetic health records (SHRs) identifies the importance and scope of this topic in the digital medicine context. The review analyzed 52 publications that met eligibility criteria, revealing privacy preservation as a primary objective, alongside class imbalance, data scarcity, and data imputation. Large language models showed superiority for medical text generation, while probabilistic models outperformed for longitudinal data, and adversarial networks excelled at generating time series data. However, the study highlights the need for a reliable performance measure to quantify SHR re-identification risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to create fake health records that can help doctors and researchers protect patients’ privacy while still using the data to improve healthcare. The authors searched through many studies and found that most of them were trying to keep personal information safe, as well as deal with problems like not having enough data or needing to fill in gaps. They also found out which kinds of models work best for creating different types of fake health records. But they still need to figure out how to measure how well these fake records are doing. |
Keywords
» Artificial intelligence » Text generation » Time series
