Temporal Label Smoothing for Early Event Prediction
Authors: Hugo Yèche, Alizée Pace, Gunnar Ratsch, Rita Kuznetsova
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By focusing the objective on areas with a stronger predictive signal, TLS improves performance over all baselines on two large-scale benchmark tasks. Gains are particularly notable along clinically relevant measures, such as event recall at low false-alarm rates. |
| Researcher Affiliation | Academia | 1Department of Computer Science, ETH Zürich, Switzerland 2ETH AI Center, ETH Zürich, Switzerland 3Max Planck Institute for Intelligent Systems, Tübingen, Germany 4Swiss Institute for Bioinformatics, Zürich, Switzerland. |
| Pseudocode | Yes | def get_smoothed_labels(event_label_patient, smoothing_fn, h_true, h_min, h_max, **kwargs): # Find when event label changes diffs = np.concatenate([np.zeros(1), event_label_patient[1:] event_label_patient[:-1]], axis=-1) pos_event_change = np.where((diffs == 1) & (event_label_patient == 1))[0] # Handle patients with no events if len(pos_event_change) == 0: pos_event_change = np.array([np.inf]) # Compute distance to closest event for each time point time_array = np.arange(len(event_label_patient)) dist_all_event = pos_event_change.reshape(-1, 1) time_array dist_to_closest = np.where(dist_all_event > 0, dist_all_event, np.inf).min(axis=0) return smoothing_fn(dist_to_closest, h_true=h_true, h_min=h_min, h_max=h_max, **kwargs) |
| Open Source Code | Yes | All code is made publicly available at https://github.com/ratschlab/tls. |
| Open Datasets | Yes | Our work is first evaluated on the prediction of acute circulatory failure within the next h = 12 hours, as defined in the Hi RID-ICU-Benchmark (Hi B) [26]. This task is based on the publicly available Hi RID dataset [7]... We use the framework defined in the MIMIC-III Benchmark (M3B) [35] for the MIMIC-III dataset [37]... |
| Dataset Splits | No | Hyperparameters introduced by baselines or by our method, such as strength term γ in smoothing parametrization qexp, are optimized through grid searches on the validation set. |
| Hardware Specification | Yes | We trained all models on a single NVIDIA RTX2080Ti with a Xeon E5-2630v4 core. |
| Software Dependencies | Yes | A full list of libraries and the version we used is provided in the environment.yml file. The main libraries on which we build our experiments are the following: pytorch 1.11.0 [50], scikit-learn 0.24.1[51], ignite 0.4.4, CUDA 10.2.89[52], cud NN 7.6.5[53], gin-config 0.5.0 [54]. |
| Experiment Setup | Yes | For all models, we set the batch size according to the available hardware capacity. Because transformers are memory-consuming, we train the decompensation models with a batch size of 8 stays. On the other hand, we train the GRU model for circulatory failure with a batch size of 64... Exact parameters are reported in Table 6... Table 6: Hyperparameter search range for circulatory failure with GRU [39] backbone... Learning Rate (1e-5, 3e-5, 1e-4, 3e-4) Drop-out (0.0, 0.1, 0.2, 0.3, 0.4) Depth (1, 2, 3) Hidden Dimension (32, 64, 128, 256) L1 Regularization (1e-2, 1e-1, 1, 10, 100). |