EHR2Vec

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EHR2Vec is a embedding translation tool for medical entities based on attention mechanism. We have provided a version of tensorflow implement and it is constantly being provided.

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# EHR2Vec
EHR2Vec is an embedding translation tool for medical entities based on attention mechanism. We have provided a version of tensorflow implement and it is constantly being improved.

# Prerequisites
1.	We use Python 3.6 and Tensorflow 1.8.0. 
2.	Download/clone the EHR2Vec code

# Running
1.	Preparing training data:
  Training data should be a list of list of medical entities include diagnosis, medication, labtest and symptom and the medical entities should be transformed to integers. In our model, a patient medical event includes many medical entities and this medical events of a patient constitute a patient complete medical process. In the meantime, a patient medical events are sorted in chronological order. So the out layer list denote a patient, each inner layer denote each event of this patient and we use -1 to separate every event of a patient. For example, [[22,50,33], [-1] ,[4,58,60],[20]] means there are two patients where the first patient only had one event which include three entities and the second patient had two event include [4,58,60] and [20]. And you should save the transformation vocabulary (from medical entities to integers). In our model, the length of each event is fixed and we use 0 as padding value. So 0 should also be saved in the transformation vocabulary which means padding. Both of the two files need to be pickled use python pickle.

2.	Model hyper-parameters configuration:
   The max length of the medical event of all patients needs to be count and called maxlen_seqs in our model. The number of all your medical entities in your vocabulary is required in our model and called n_input. The default dimension of entity is 512 which you can change as you like. The attention mechanism parameters include num_heads, num_blokcs and d_ff you can set. The number of epoch and the size of batch should be configured for your own machine.

3.	Running:
You can train the model with the default hyper-parameters except the training data path, n_input, maxlen_seqs, dict_types_path, save_model_path and embedding save path. And you can use the simple execution command to run the model:

python3 SLE_EHR2Vec_Runner.py --data_path  \<your train data path\>  --n_input \<the entity number of your data\>  --maxlen_seqs  \<the max event length of your data\>  --dict_types_path \<your vocabulary path\> --entity_embedding_path  \<save embedding path\>  --save_model_path  \<save your model path\>

The complete execution command includes all the hyper-parameters：

python3 SLE_EHR2Vec_Runner.py --data_path <your train data path>  --n_input <the entity number of your data>  --maxlen_seqs  <the max event length of your data>  --d_model  <dimension of entity embedding> --d_ff  <number of neurons of feedword network> --num_blocks <number of blocks> -- num_heads  <head number of the multi-head attenion>  --dropout_rate < dropout rate> --dict_types_path <your vocabulary path>  --entity_embedding_path  <save embedding path>  --save_model_path  <save your model path>  --max_epoch <number of epoch>  --batch_size <batch size> --display_step <display frequency of the training process>

## Example of how to run EHR2Vec with the provide train_data_example.pkl and dict_types.pkl

1、	Count the total number of input entities(n_input) and the max length(max_seq_length) of all events with the command.

python3 statistic.py --dict_types ./example/dict_types.pkl --example_train_path ./example/train_data_example.pkl
    
    You will get the n_input number and max_seq_length number from the output.

2、	Run the EHR2Vec model and get the entity embedding:

python3 SLE_EHR2Vec_Runner.py --n_input 17 --maxlen_seqs 7 --data_path ./example/train_data_example.pkl --dict_types_path ./example/dict_types.pkl --save_model_path ./example/EHR2Vec_model/ --entity_embedding_path ./example/entity_embedding.pkl

3、The vectors result can be found in the entity_embedding_path.

EHR2Vec

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