trial_outcome.SPOT

class pytrial.tasks.trial_outcome.spot.SPOT(num_topics=50, n_trial_projector=2, n_timestemp_projector=2, n_rnn_layer=1, criteria_column='criteria', batch_size=1, n_trial_per_batch=None, learning_rate=0.0001, weight_decay=0.0001, epochs=10, evaluation_steps=50, warmup_ratio=0, device='cuda:0', seed=42, output_dir='./checkpoints/spot')[source]

Implement sequential predictive modeling of clinical trial outcome with meta-learning (SPOT) 1.

Parameters

  • num_topics (int) – Number of topics to discover.

  • n_trial_projector (int) – Number of layers in the trial projector.

  • n_timestemp_projector (int) – Number of layers in the timestamp projector.

  • n_rnn_layer (int) – Number of layers in the RNN for encoding ontology & smiles strings.

  • criteria_column (str) – The column name of the criteria in the input data.

  • batch_size (int) – Batch size for training.

  • n_trial_per_batch (int) – Number of trials in each batch. If None, use all trials in the topic in each batch.

  • learning_rate (float) – Learning rate for training.

  • weight_decay (float) – Regularization strength for l2 norm; must be a positive float.

  • epochs (int) – Number of training epochs.

  • evaluation_steps (int) – Number of steps to evaluate the model.

  • warmup_ratio (float) – Warmup ratio for learning rate scheduler.

  • device (str) – Device to use for training and inference.

  • seed (int) – Random seed.

Notes

1

Wang, Z., Xiao, C., & Sun, J. (2023). SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning. arXiv preprint arXiv:2304.05352.

add_trials(seqtrain, df)[source]

Add new trials to the SequenceTrial object. It is used for the validation and test data.

Parameters

  • seqtrain (model_utils.data_structure.SequenceTrial) – The sequence of clinical trials represented by SequenceTrial object.

  • df (pd.DataFrame) – The dataframe of new trials that should be added to the input seqtrain.

fit(train_data, valid_data=None)[source]

Train the SPOT model. It has two/three steps: 1. Encode the criteria using pretrained BERT to get the criteria embedding (optional). 2. Discover topics from the training data and transform the data into a SequenceTrial object. 3. Train the SPOT model.

Parameters

  • train_data (TrialOutcomeDataset) – Training data, should be a TrialOutcomeDataset object.

  • valid_data (TrialOutcomeDataset) – Validation data, should be a TrialOutcomeDataset object.

load_model(input_dir=None)[source]

Load the model from a directory.

Parameters

input_dir (str or None) – The directory to load the model.

predict(data, target_trials=None)[source]

Predict the outcome of a clinical trial.

Parameters

  • data (TrialOutcomeDataset) – A TrialOutcomeDataset object containing the data to predict.

  • target_trials (list) – A list of trial ids to predict. If None, all trials in data will be predicted.

save_model(output_dir=None)[source]

Save the model to a directory.

Parameters

output_dir (str or None) – The directory to save the model. If None, use the default directory ‘./checkpoints’.

transform_topic(df, df_val=None, df_test=None)[source]

Transform the data into a SequenceTrial object.

Parameters

  • df (pandas.DataFrame) – A dataframe containing the training data.

  • df_val (pandas.DataFrame) – A dataframe containing the validation data.

  • df_test (pandas.DataFrame) – A dataframe containing the test data.