Input the following code to create the virtual environment MtiRec

conda env create -f environment.yaml

│ DHTMRec.py            # Training + prediction

│ Environment.YAMl      # Environment

│  README.md            

├─data                  # Folder : store raw data

│ *1

│

├─model

│ │ assessment_criteria.py    # Evaluation metrics

│ │ data_process.py           # Data processing

│ │ DHtm.py                   # Topic model : DHTM

│ │ mti_rec.py                # Some tools 

│ └─__pycache__               # Store '.pyc' files

│

└─result_data                 # Save results related to model training and prediction
  dhtm_model.pkl              # A trained model dhtm
  terms.xlsx                  # AVPs (*2)
  test_df.xlsx                # Test set  (*2)
  train_df.xlsx               # Training set (*2)
  Accuracy_lambda50_weight20_bench5.xlsx 

*1: data can be downloaded from the BCDB's official website: http://bcdb.mdt.team:8080

*2: Only tiny data as an example

Input the following code to run the project

python DHTMRec.py

The following step information is already prompted in the code

Step1: Pack patient's non-null attributes as a document, which is the set of attribute-value pairs (AVPs).

Step2: Train the topic model, which named DHTM, to explore the connections between AVPs and therapies.

Step3: Combine therapies in different categories to form therapy tuples.

Step4: Calculate the connections between patients and therapy tuples by DHTM.

Step5: Calculate the connections between patients and therapy tuples by their same key AVPs.

Step6: Combine these two kinds of connections by a weighted fusion.

Step7: Calculate the accumulated gain of null attribute for patients.

Step8: Recommend attributes and evaluate the performance of the model on three metrics.

Prompts for the completion of dataset processing, Gibbs sampling progress, tuple information, etc.

The DHTM model is saved in the "result_data" folder as "dhtm_model.pkl".

Save the evaluation metrics Accuracy and MRR of the test set in the "result_data" folder. The file name should correspond to the parameter settings for the evaluation metrics results, such as "Accuracy_lambda50_weight20_bench5.xlsx", with the respective parameters being 0.5, 0.2, and 5.

1. Due to privacy protection and other reasons, medical data is not uploaded. You can apply for downloading from the BCDB official website (http://bcdb.mdt.team:8080).

2. DHTMRec.py provides a multi-threading method for model performance evaluation to determine the optimal parameters for custom datasets.

Symbols in papers: Variables in code

$\omega$ ：weight_t1

$\eta$ ： lambda_df

$\chi$：num_PCs

$p'({\Delta | m})$：new_df2

$p(\Delta | m)$ ： new_df1，new_df

$R_{m,l}$：gain_value