v1xerunt/DocTr

The source code for DocTr: Automated Matching of Clinicians to Clinical Trials Utilizing Patient Resource Insights from Real-World Evidence

DocTr

Source code for paper DocTr: Optimizing Clinical Trial Site Selection using Open Payments and Patient Encounter Data

Requirements

• Install python, pytorch and RecBole. We use Python 3.7.6, Pytorch 1.12.1.
• We use the Clinical-Trial-Parser to parse trial criteria from https://github.com/facebookresearch/Clinical-Trial-Parser/tree/main.
• If you plan to use GPU computation, install CUDA
• The composite similarity metric need to be manually added to the RecBole/evaluator/metrics.py. The metrics calculation function is in utils.py/com_sim.

Data resources

All data should be downloaded in the data folder.

Public external data

• The CMS Open payments data https://www.cms.gov/priorities/key-initiatives/open-payments/data/dataset-downloads. We use the OP_DTL_RSRCH_PGYRXXXX_P01202023.csv from 2017-2021.
• US State level zipcode mapping file uszips.csv from https://github.com/akinniyi/US-Zip-Codes-With-City-State/tree/master
• Trial XML files from https://clinicaltrials.gov/

We have provided some processed data in the data folder. They can be read using pickle.read Some key files are:

• npi2trial.pkl: The linked relationship between NPI and NCTID.
• npi_info_dict.pkl: The clinician information extracted from CMS data, including location information and other public information.
• payment_dict.pkl: The processed CMS dataset. Recording the payment record from each trial identified by NCTID to each clinician or teaching hospital identified by NPI.
• ie_extracted_clinical_trials.tsv: The processed trial criteria using the Clinical-Trial-Parser.

Notebook files

01 - Data preprocessing

• 01_A_process_payment_data.ipynb: Extract the clinical trial and clinician relationship from the OpenPayment data.
• 01_B_process_trial_info.ipynb: Parse clinical trial information from trial XML documents.
• 01_C_process_trial_criteria_embd.ipynb: Generate the trial criteria embeddings using ClinicalBERT.
• 01_D_process_trial_summary_embd.ipynb: Generate the trial summary embeddings using ClinicalBERT.
• 01_E_process_claims_data.ipynb: Process the ICD codes in the claims data.
• 01_F_process_clinician_info.ipynb: Extract the clinician information from the CMS data.
• 01_G_process_geo_data.ipynb: Extract demographics information (e.g., racial and ethnicity distributions) from the regional data.

02 - Data linkage

• 02_A_gen_trial_npi_relation.ipynb: Link and filter trials and clinicians information.
• 02_B_get_trial_phase.ipynb: Get trial phase and condition information.
• 02_C_get_stat.ipynb: Get basic data statistics of the dataset we built.

03 - Builiding recommendation dataset

• 03_A_gen_atom_file.ipynb: Build the atomic dataset under regular setting for recommendation model training, based on the requirement of the RecBole package.
• 03_A_gen_zeroshot_atom_file.ipynb: Build the atomic dataset under temporal setting for recommendation model training, based on the requirement of the RecBole package.

04 - Model training and evaluations

• 04_train_doctr.ipynb: We build and evaluate the proposed DocTr model.

05 - Additional analysis

• 05_A_get_competing_trial.ipynb: We extract the competing trials from the trial relationships.
• 05_B_fairness_analysis.ipynb: We run the genetic algorithm to improve the fairness of the recommendation results, and report the results. The genetic algorithm is in genetic.py.