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 | Conference PDF | Archive PDF | Plain Text | 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. |