Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling
Authors: Xi Yang, Yuan Zhang, Min Chi
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of MT-TICC is first validated via a case study using a real-world hand gesture dataset with ground-truth labels. Then we further apply it for sepsis progression modeling using EHRs. The results suggest that MT-TICC can significantly outperform competitive baseline models, including the TICC. |
| Researcher Affiliation | Academia | Xi Yang , Yuan Zhang , Min Chi Department of Computer Science, North Carolina State University {yxi2, yzhang93, mchi}@ncsu.edu |
| Pseudocode | No | The paper describes the optimization steps and methodology in narrative form but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The hand gesture dataset we employed in this case study contains multichannel surface electromyographic (s EMG) signals collected from 36 participants, each of whom performed a series of hand gestures twice [Lobov et al.2019]. [Lobov et al., 2019] Sergey Lobov, Nadia Krilova, et al. EMG data for gestures Data Set, 2019. https://archive.ics.uci.edu/ml/datasets/EMG+data+for+gestures#. |
| Dataset Splits | Yes | We repeated the 5-fold cross-validation ten times and conducted a corrected paired t-test [Nadeau and Bengio2003] to compare MT-TICC and M-TICC against the TICC. All models were evaluated by repeating the 3-fold cross-validation ten times. |
| Hardware Specification | Yes | Given our entire EHRs (4,224,567 events with 14 features), it converges in 20 iterations with each iteration costing 120s (Intel i7-8700k with 32GB memory). |
| Software Dependencies | No | The paper states 'We implemented LSTM with Keras' but does not provide specific version numbers for Keras or any other software dependencies. |
| Experiment Setup | Yes | In MT-TICC, the cluster number K was 11; the window size ω was 2; the sparsity and consistency coefficients λ and β were 1e-5 and 4, respectively. for example, in MT-TICC, the window size ω was 3; the sparsity and consistency coefficients λ and β were 1e-8 and 10, respectively. We implemented LSTM with Keras and tuned the parameters by grid search. |