This package serves as basis for the paper: "Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking" by Wojciech Kusa, Óscar E. Mendoza, Petr Knoth, Gabriella Pasi, Allan Hanbury

Enhancing patient recruitment for clinical trials is often hampered by the complexity of matching patient descriptions with trial eligibility criteria. This repository presents a novel approach to facilitating this process. By employing a two-stage model, we first use a data enrichment technique to boost disease and drug mentions in both queries and trial documents. Subsequently, a novel re-ranking method employing a BERT-based neural model is adapted specifically for this task, drawing on the structure of trial documents. Our method has shown to significantly improve the precision in retrieving relevant clinical trials by 15%. Explore our code and findings to gain insights into our approach and its implications for clinical trial matching.

1. Installation
2. Data
3. Usage
4. Citing

Tested with python=3.8. Assuming you have conda installed, create new environment with:

$ conda create --name patient-trial-matching python=3.8

Activate the environment:

$ conda activate patient-trial-matching

Install requirements:

(patient-trial-matching)$ pip install -r requirements.txt
(patient-trial-matching)$ git submodule update --init --recursive
(patient-trial-matching)$ pip install -e clinical-trials 
(patient-trial-matching)$ pip install spacy==3.1.6   # this will raise a warning, but it is needed for the models to properly load
(patient-trial-matching)$ pip install medspacy==0.2.0.0
(patient-trial-matching)$ pip install medspacy==0.2.0.1 
(patient-trial-matching)$ pip install pydantic==1.10.11 

This will install all required packages and also this project in a devel mode.

Install and launch redis server:

redis-server

If your system does not support redis, the code will use the mockup version of the redis server.

Patients and clinical trials information can be downloaded from TREC-CDS website:

• topics2021.xml file with 75 patients' data
• topics2022.xml file with 50 patients' data
• 5 .zip files with ClinicalTrials data

ClinicalTrials XMLs should be extracted into data/external/ClinicalTrials/ folder.

To download qrels and NER model for detecting age and gender run:

(patient-trial-matching)$ python prepare_data.py

Prepare patients' data. It will prepare 2 files: topics2021.jsonl and topics2022.jsonl in output_folder folder.

(patient-trial-matching)$ python trec_cds/data/convert_trials_to_jsonl.py --input_folder PATH_TO_PATIENT_XML_DATA --output_folder PATH_TO_OUTPUT_FOLDER

Prepare trials' data. This might take several hours (5-10 hours) as the entity extraction model is making predictions for each trial.

(patient-trial-matching)$ python trec_cds/data/convert_trials_to_jsonl.py --input_data PATH_TO_UNZIPPED_XML_DATA --outfile PATH_TO_OUTPUT_FOLDER

Both these scripts will parse data and extract drug and disease entities. Processed output will be generated in a jsonl format.

The experiments for input fields and extracted keywords below are for the BM25 and BM25 models. To run other models ( DFR, TF-IDF), you need to install pyTerrier and run the corresponding terrier scripts.

To run the lexical matching experiment, run:

(patient-trial-matching)$ python scripts/input_fields_experiment.py --topic_file data/external/topics2021.xml --clinical_trials_folder PATH_TO_UNPROCESSED_CLINICAL_TRIALS  --binary_qrels data/external/qrels2021_binary.txt --graded_qrels data/external/qrels2021.txt --results_folder RESULTS_OUTPUT_FOLDER  --submission_folder TREC_SUBMISSION_FOLDER

To run the experiment measuring impact of extracted keywords, run:

(patient-trial-matching)$ python scripts/extracted_keywords_experiment.py --trials_file PATH_TO_TRIALS_JSONL_FILE --topic_file data/external/topics2021.jsonl  --binary_qrels data/external/qrels2021_binary.txt --graded_qrels data/external/qrels2021.txt --results_folder RESULTS_OUTPUT_FOLDER  --submission_folder TREC_SUBMISSION_FOLDER 

Postprocessing script is based on the detected gender and age of patients. It will filter out trials that are not suitable for a given patient.

(patient-trial-matching)$ python scripts/filtering_experiment.py --topic_file data/external/topics2021.jsonl --binary_qrels data/external/qrels2021_binary.txt --graded_qrels data/external/qrels2021.txt --runs_folder PATH_TO_FOLDER_WITH_PREVIOUS_STEP_OUTPUTS --output_folder RESULTS_OUTPUT_FOLDER 

All parameters and configurations are stored in yml files inside config folder. Before running the models ensure that these configs are pointing to correct data paths.

To train the neural model, run:

(patient-trial-matching)$ python neural/models/train_crossencoder.py

To train the neural model, run:

(patient-trial-matching)$ python neural/models/further_train_crossencoder.py

To run inference, run:

(patient-trial-matching)$ python neural/models/predict_crossencoder.py

If you find our code useful, please cite our paper:

@article{Kusa2023Effective,
title = {Effective matching of patients to clinical trials using entity extraction and neural re-ranking},
journal = {Journal of Biomedical Informatics},
pages = {104444},
year = {2023},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2023.104444},
url = {https://www.sciencedirect.com/science/article/pii/S153204642300165X},
author = {Wojciech Kusa and Óscar E. Mendoza and Petr Knoth and Gabriella Pasi and Allan Hanbury}
}