Summary of Modeling Long Sequences in Bladder Cancer Recurrence: a Comparative Evaluation Of Lstm,transformer,and Mamba, by Runquan Zhang et al.
Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba
by Runquan Zhang, Jiawen Jiang, Xiaoping Shi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
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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 new study aims to improve the analysis of complex time-dependent data in survival models by evaluating three long-sequence models: LSTM, Transformer, and Mamba. The researchers integrate these models with the Cox proportional hazards model to enhance performance in analyzing recurrent events with dynamic time features. The study compares the ability of different models to extract and utilize features from time-dependent clinical recurrence data and finds that the LSTM-Cox model outperformed both the Transformer-Cox and Mamba-Cox models in prediction accuracy and model fit, achieving a Concordance index of up to 0.90 on the test set. The study also identifies significant predictors of bladder cancer recurrence, such as treatment stop time, maximum tumor size at recurrence, and recurrence frequency, which align well with clinical outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study helps predict when people will get sick again. They compare three computer models to see how well they can analyze data about when someone has an event, like a tumor recurring. The best model was the LSTM-Cox model, which did better than two other models at predicting what would happen next. This model is good at finding important patterns in the data that doctors can use to make decisions about treatment. The study shows that this model is helpful for predicting when someone will have cancer come back and could be used to help doctors decide on the best course of treatment. |
Keywords
» Artificial intelligence » Lstm » Transformer
