An hybrid recommender system to predict the effectiveness of therapies in curing a medical condition of a patient. Check the report for more details on the implementation.

The system is based on a combination of different methods:

• Latent Factorization Models (SVD/SVD++)
• Collaborative Filtering (user-user/item-item)
• Nearest Neighbors

Clone the repository and install the required dependencies:

git clone https://github.com/materight/therapy-recommender-system.git
cd therapy-recommender-system
pip install -r requirements.txt

To produce recommendations for a patient and a condition, run:

python main.py -d [dataset_path] -p [patient_id] -c [condition_id]

Alternatively, it is possible to specify a test csv file containing the patient and condition ids to predict:

python main.py -d [dataset_path] -t [test_path]

Additional options can be specified (run with --help to see the available ones).

To evaluate multiple configurations for the recommender and perform an ablation study,run:

python benchmark.py -d [dataset_path]

The results will be saved in a csv file under the ./results folder.

The --val_split [fraction] option can be specified to customize the size of the validation split to use for evaluation.

A synthetic dataset can be generated by simply running:

python data_generator.py --n-patients [num_generated_patients] -o [output_path]

The generation can be customized with additional options (use --help to see the available ones).