We lifted 7 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| vanderschaarlab/tphenotype | canonical | 4 of 5 |
| yvchao/tphenotype | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| explain | Ran | yvchao/tphenotype/src/tphenotype/model/cluster_explainer.py code served (permissive licence) · get_code("d66a693c33a4ff2a") |
| find_similar_series | Ran | vanderschaarlab/tphenotype/src/tphenotype/model/encoder.py code served (permissive licence) · get_code("4eaa269c20023427") |
| fit_cluster | Ran | yvchao/tphenotype/src/tphenotype/model/cluster_explainer.py code served (permissive licence) · get_code("7fecd88a4d8369c2") |
| initialize_centers | Ran | vanderschaarlab/tphenotype/src/tphenotype/model/graph_kmeans.py code served (permissive licence) · get_code("81808a528fc22fc8") |
| pairwise_distances | Ran | vanderschaarlab/tphenotype/src/tphenotype/model/encoder.py code served (permissive licence) · get_code("a10e9d8f0ae8d6ee") |
| slice_sub_sequences | Ran | vanderschaarlab/tphenotype/src/tphenotype/baselines/kmdtw.py code served (permissive licence) · get_code("9cd7f2e8e3aa1f27") |
| d_js | Not yet run | vanderschaarlab/tphenotype/src/tphenotype/model/graph_kmeans.py code served (permissive licence) · get_code("c75bfa9b609f4ded") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Clustering time-series data in healthcare is crucial for clinical phenotyping to understand patients' disease progression patterns and to design treatment guidelines tailored to homogeneous patient subgroups. While rich temporal dynamics enable the discovery of potential clusters beyond static correlations, two major challenges remain outstanding: i) discovery of predictive patterns from many potential temporal correlations in the multi-variate time-series data and ii) association of individual temporal patterns to the target label distribution that best characterizes the underlying clinical progression. To address such challenges, we develop a novel temporal clustering method, T-Phenotype, to discover phenotypes of predictive temporal patterns from labeled time-series data. We introduce an efficient representation learning approach in frequency domain that can encode variable-length, irregularly-sampled time-series into a unified representation space, which is then applied to identify various temporal patterns that potentially contribute to the target label using a new notion of path-based similarity. Throughout the experiments on synthetic and real-world datasets, we show that T-Phenotype achieves the best phenotype discovery performance over all the evaluated baselines. We further demonstrate the utility of T-Phenotype by uncovering clinically meaningful patient subgroups characterized by unique temporal patterns.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.12619")
get_code_for_paper("2302.12619")
have("2302.12619")
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