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..
iSurvive: An Interpretable, Event-time Prediction Model for mHealth
Authors: Walter H. Dempsey, Alexander Moreno, Christy K. Scott, Michael L. Dennis, David H. Gustafson, Susan A. Murphy, James M. Rehg
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show promising results both in simulation and on a real-world m Health recovery support services dataset from individuals with substance use disorders. and For each experiment that follows, we perform a crossvalidation based assessment of prediction accuracy. and Figure 2. Cross-validated complete Brier score on recovery support services study for several discriminative models and i Survive |
| Researcher Affiliation | Collaboration | 1 University of Michigan 2 Georgia Institute of Technology 3 Lighthouse Institute 4 University of Wisconsin Madison. |
| Pseudocode | Yes | Algorithm 2 Event prediction algorithm |
| Open Source Code | Yes | Our publicly-released software (http://cbi.gatech.edu/Survival-HMM) will enable the m Health and data science communities to benefit from these new modeling capabilities. |
| Open Datasets | No | We analyze a set of recovery support studies on individuals with substance use disorders (SUDs). In particular, we analyze two pilot studies a 5-week study of adults (N = 23) and a 6-week study of adolescents (N = 29)... The paper describes a specific collected dataset for their case study but does not provide access information (link, DOI, citation) to make it publicly available. |
| Dataset Splits | Yes | For each experiment that follows, we perform a cross-validation based assessment of prediction accuracy. We randomly partition the N participants into groups of size K. Suppose N/K = M and we label each partition uniquely m = 1, . . . , M |
| Hardware Specification | No | The paper mentions 'smartphones' used for data collection but does not specify any hardware (e.g., GPU, CPU models, or cloud instances) used to run the experiments or train the models. |
| Software Dependencies | No | The paper discusses algorithms and models (e.g., GLM, CT-HMM, EM algorithm, Fisher scoring) but does not specify any software libraries or dependencies with version numbers. |
| Experiment Setup | No | The paper describes the model architecture, estimation methods (EM), and initialization strategies, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or system-level training settings. |