Update README.md

Author: Andy-jqa

README.md

@@ -1,4 +1,9 @@
-# TrialGPT
+# TrialGPT: Matching Patients to Clinical Trials with Large Language Models
+
+## Introduction
+
+Clinical trials are often hindered by the challenge of patient recruitment. In this work, we introduce TrialGPT, a first-of-its-kind large language model (LLM) framework to assist patient-to-trial matching. Given a patient note, TrialGPT predicts the patient’s eligibility on a criterion-by-criterion basis and then consolidates these predictions assess the patient’s eligibility for the target trial. We evaluate the trial-level prediction performance of TrialGPT on three publicly available cohorts of 184 patients with over 18,000 trial annotations. We also engaged three physicians to label over 1,000 patient-criterion pairs to assess its criterion-level prediction accuracy. Experimental results show that TrialGPT achieves a criterion-level accuracy of 87.3% with faithful explanations, close to the expert performance (88.7%–90.0%). The aggregated TrialGPT scores are highly correlated with human eligibility judgments, and they outperform the best-competing models by 32.6% to 57.2% in ranking and excluding clinical trials. Furthermore, our user study reveals that TrialGPT can significantly reduce the screening time (by 42.6%) in a real-life clinical trial matching task. These results and analyses have demonstrated promising opportunities for clinical trial matching with LLMs such as TrialGPT.
+
 
 ## Configuration
 
@@ -17,6 +22,39 @@ config = {
 }
 ```
 
+## Datasets
+
+We provide the pre-processed datasets of three publicly available cohorts in `./datasets`, including:
+- `./datasets/trial_sigir.json` for the SIGIR cohort
+- `./datasets/trial_2021.json` for the TREC Clinical Trials 2021 cohort
+- `./datasets/trial_2022.json` for the TREC Clinical Trials 2022 cohort
+
+We also put a pre-processed set of the used clinical trials in `./datasets/trial2info.json`.
+
+## Step 1: Criterion-level Prediction
+
+The first step of TrialGPT is to generate the criterion-level predictions, which include (1) the explanation of patient-criterion relevance, (2) locations of relevant sentences, and (3) the eligibility predictions.
+
+Run the following code to get the GPT-4-based TrialGPT results for the three cohorts:
+```bash
+# format python run_matching.py {split} {model}
+python run_matching.py sigir gpt-4
+python run_matching.py 2021 gpt-4
+python run_matching.py 2022 gpt-4
+```
+
+## Step 2: Trial-level Aggregation
+
+The second step of TrialGPT is to aggregate the criterion-level predictions to get trial-level scores, including one score for relevance and one score for eligibility.
+
+Please make sure that the step 1 results are ready before running the step 2 code:
+```bash
+# format python run_aggregation.py {split} {model}
+python run_aggregation.py sigir gpt-4
+python run_aggregation.py 2021 gpt-4
+python run_aggregation.py 2022 gpt-4
+```
+
 ## Acknowledgments
 
 This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine.