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Objective: Retrieval-based Clinical Decision Support (ReCDS) can aid clinicalworkflow by providing relevant literature and similar patients for a givenpatient. However, the development of ReCDS systems has been severely obstructedby the lack of diverse patient collections and publicly available large-scalepatient-level annotation datasets. In this paper, we aim to define andbenchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) andPatient-to-Patient Retrieval (ReCDS-PPR) using a novel dataset calledPMC-Patients. Methods: We extract patient summaries from PubMed Centralarticles using simple heuristics and utilize the PubMed citation graph todefine patient-article relevance and patient-patient similarity. We alsoimplement and evaluate several ReCDS systems on the PMC-Patients benchmarks,including sparse retrievers, dense retrievers, and nearest neighbor retrievers.We conduct several case studies to show the clinical utility of PMC-Patients.Results: PMC-Patients contains 167k patient summaries with 3.1M patient-articlerelevance annotations and 293k patient-patient similarity annotations, which isthe largest-scale resource for ReCDS and also one of the largest patientcollections. Human evaluation and analysis show that PMC-Patients is a diversedataset with high-quality annotations. The evaluation of various ReCDS systemsshows that the PMC-Patients benchmark is challenging and calls for furtherresearch. Conclusion: We present PMC-Patients, a large-scale, diverse, andpublicly available patient summary dataset with the largest-scale patient-levelrelation annotations. Based on PMC-Patients, we formally define two benchmarktasks for ReCDS systems and evaluate various existing retrieval methods.PMC-Patients can largely facilitate methodology research on ReCDS systems andshows real-world clinical utility.

PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems