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..
DDL: Deep Dictionary Learning for Predictive Phenotyping
Authors: Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations on multiple EHR datasets demonstrated that DDL outperforms the existing predictive phenotyping methods on a wide variety of clinical tasks that require patient phenotyping. |
| Researcher Affiliation | Collaboration | 1 Department of Computational Science and Engineering, Georgia Institute of Technology 2MIT-IBM Watson AI Lab, IBM Research 3 Analytics Center of Excellence, IQVIA |
| Pseudocode | Yes | Algorithm 1 DDL (Tmax max no. of optimizing iterations) |
| Open Source Code | Yes | 2Code is available at https://github.com/futianfan/dictionary. |
| Open Datasets | Yes | on 3 healthcare datasets, Heart Failure (HF) [Ma et al., 2018], MIMIC-III and a subset of Truven Market Scan Data1, which contain 16794, 58000 and 72179 EHR samples, respectively. |
| Dataset Splits | Yes | For each experiment, we randomly generate 5 different partitions of the entire dataset into training, validation and testing sets with a 7 : 1 : 2 ratio. |
| Hardware Specification | Yes | Our method is implemented by Tensorflow 1.9.0 and Python 3.52; and tested on an Intel Xeon E5-2690 machine with 256G RAM and 8 NVIDIA Pascal Titan X GPUs. |
| Software Dependencies | Yes | Our method is implemented by Tensorflow 1.9.0 and Python 3.52 |
| Experiment Setup | Yes | For HF dataset, the no. of hidden units of DDL s RNN component is set to 100. Its dictionary size is set to 10. Its learning rate for gradient back-propagation on the aggregate loss (Eq. 7) is set to be 1e 2. To trade-off between projection, reconstruction and prediction losses, we set ηd = 1e 1, ηc = 1 and ηr = 1e 3 in Eq. 7. For the projection loss Ld in Eq. 2, the regularization hyper-parameters are set as λ1 = 5e 2 and λ2 = 1e 3. For MIMIC-III dataset, we use the same configuration but with the following minor changes on learning rate (5e 2) and trade-off coefficients (ηd = ηc = 1 and ηr = 2e 3) between individual losses of DDL. For TRUVEN dataset, we also use the similar configuration but with the dictionary and RNN component s hidden sizes set to be 15 and 200, respectively. In addition, the trade-off coefficients in Eq. 7 are also adjusted to ηd = 1e 1 and ηc = ηr = 1. The batch sizes of DDL s stochastic gradient descent on HF and MIMIC-III are both set to be 32, while on TRUVEN, it is set to be 64 (since TRUVEN dataset is larger than the others). |