Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Attentive State-Space Modeling of Disease Progression
Authors: Ahmed M. Alaa, Mihaela van der Schaar
NeurIPS 2019 | Venue PDF | 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 EMAIL Mihaela van der Schaar UCLA, University of Cambridge, and Alan Turing Institute {EMAIL,EMAIL} |
| 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. |