Attentive State-Space Modeling of Disease Progression
Authors: Ahmed M. Alaa, Mihaela van der Schaar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on data from the UK Cystic Fibrosis registry show that our model demonstrates superior predictive accuracy, in addition to providing insights into disease progression dynamic. |
| Researcher Affiliation | Academia | Ahmed M. Alaa ECE Department UCLA ahmedmalaa@ucla.edu Mihaela van der Schaar UCLA, University of Cambridge, and Alan Turing Institute {mv472@cam.ac.uk,mihaela@ee.ucla.edu} |
| Pseudocode | No | The paper describes its methods in prose and with figures (e.g., Figure 2, Figure 3) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We implemented our model using Tensorflow1. The code is provided at https://bitbucket.org/mvdschaar/mlforhealthlabpub. |
| Open Datasets | Yes | We used data from a cohort of patients enrolled in the UK CF registry, a database held by the UK CF trust2." and footnote "2https://www.cysticfibrosis.org.uk/the-work-we-do/uk-cf-registry/ |
| Dataset Splits | Yes | All prediction results reported in this Section where obtained via 5-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. It only mentions software frameworks and optimization algorithms. |
| Software Dependencies | No | The paper states 'We implemented our model using Tensorflow' but does not specify a version number for Tensorflow or other software dependencies. |
| Experiment Setup | Yes | The LSTM cells in both the attention network (Figure 2) and the inference network (Figure 3) had 2 hidden layers of size 100. The model and inference networks were trained using ADAM with a learning rate of 5 10 4, and a mini-batch size of 100. The same hyperparameters setting was used for all baseline models involving RNNs. |